@article{ubo_mods_00205246, author = {Loepp, Benedikt}, title = {Multi-list interfaces for recommender systems: Survey and future directions}, journal = {Frontiers in Big Data}, year = {2023}, publisher = {Frontiers Media}, address = {Lausanne}, volume = {6}, keywords = {Recommender systems; Multi-list recommendation; Carousels; User interfaces; User experience; Choice overload; Survey}, abstract = {For a long time, recommender systems presented their results in the form of simple item lists. In recent years, however, multi-list interfaces have become the de-facto standard in industry, presenting users with numerous collections of recommendations, one below the other, each containing items with common characteristics. Netflix’s interface, for instance, shows movies from certain genres, new releases, and lists of curated content. Spotify recommends new songs and albums, podcasts on specific topics, and what similar users are listening to. Despite their popularity, research on these so-called “carousels” is still limited. Few authors have investigated how to simulate the user behavior and how to optimize the recommendation process accordingly. The number of studies involving users is even smaller, with sometimes conflicting results. Consequently, little is known about how to design carousel-based interfaces for achieving the best user experience. This mini review aims to organize the existing knowledge and outlines directions that may improve the multi-list presentation of recommendations in the future.}, note = {in press}, issn = {2624-909X}, doi = {10.3389/fdata.2023.1239705}, url = {https://doi.org/10.3389/fdata.2023.1239705}, language = {en} } @article{ubo_mods_00204805, author = {Hernandez-Bocanegra, Diana C. and Ziegler, Jürgen}, title = {Explaining Recommendations through Conversations: Dialog Model and the Effects of Interface Type and Degree of Interactivity}, journal = {ACM Transactions on Interactive Intelligent Systems (TiiS)}, year = {2023}, publisher = {Association for Computing Machinery (ACM)}, address = {New York}, volume = {13}, number = {2}, keywords = {Recommender systems; explanations; argumentation; interactive interfaces; conversational agent; dataset; intent detection; user study}, issn = {2160-6455}, doi = {10.1145/3579541}, url = {https://doi.org/10.1145/3579541}, note = {001018513000001}, language = {en} } @incollection{ubo_mods_00204623, author = {Ziegler, Jürgen and Loepp, Benedikt}, editor = {Augstein, Mirjam and Herder, Eelco and Wörndl, Wolfgang}, title = {User-centered recommender systems}, booktitle = {Personalized Human-Computer Interaction}, series = {De Gruyter Textbook}, year = {2023}, edition = {2}, publisher = {De Gruyter Oldenbourg}, address = {Berlin}, pages = {33–58}, keywords = {Recommender systems; Visualization; Interactive recommending; Explanations; Evaluation}, isbn = {9783110988567}, doi = {10.1515/9783110988567-002}, url = {https://doi.org/10.1515/9783110988567-002}, language = {en}, abstract = {Recommender systems aim at facilitating users’ search and decision-making when they are faced with a large number of available options, such as buying products online or selecting music tracks to listen to. A broad range of machine learning models and algorithms has been developed that aim at predicting users’ assessment of unseen items and at recommending items that best match their interests. However, it has been shown that optimizing the system in terms of algorithm accuracy often does not result in a correspondingly high level of user satisfaction. Therefore, a more user-centric approach to developing recommender systems is needed that better takes into account users’ actual goals, the current context and their cognitive demands. In this chapter, we discuss a number of techniques and design aspects that can contribute to increasing transparency, user understanding and interactive control of recommender systems. Furthermore, we present methods for evaluating systems from a user perspective and point out future research directions.} } @inproceedings{loepp2023how, author = {Loepp, Benedikt and Ziegler, Jürgen}, booktitle = {RecSys ’23: Proceedings of the 17th ACM Conference on Recommender Systems}, title = {How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User Perspective}, year = {2023}, address = {New York, NY, USA}, publisher = {ACM}, isbn = {9798400702419}, url = {https://doi.org/10.1145/3604915.3610638}, doi = {10.1145/3604915.3610638}, abstract = {Multi-list interfaces are widely used in recommender systems, especially in industry, showing collections of recommendations, one below the other, with items that have certain commonalities. The composition and order of these "carousels" are usually optimized by simulating user interaction based on probabilistic models learned from item click data. Research that actually involves users is rare, with only few studies investigating general user experience in comparison to conventional recommendation lists. Hence, it is largely unknown how specific design aspects such as carousel type and length influence the individual perception and usage of carousel-based interfaces. This paper seeks to fill this gap through an exploratory user study. The results confirm previous assumptions about user behavior and provide first insights into the differences in decision making in the presence of multiple recommendation carousels.} } @inproceedings{kleemann2023, abstract = {Today’s e-commerce websites often provide many different components, such as filters and conversational product advisors, to help users find relevant items. However, filters and advisors are often presented separately and treated as independent entities so that the previous input is discarded when users switch between them. This leads to memory loads and disruptions during the search process. In addition, the reasoning behind the advisors’ results is often not transparent. To overcome these limitations, we propose a novel approach that exploits a graph structure to create an integrated system that allows a seamless coupling between filters and advisors. The integrated system utilizes the graph to suggest appropriate filter values and items based on the user’s answers in the advisor. Moreover, it determines follow-up questions based on the filter values set by the user. The interface visualizes and explains the relationship between a given answer and its relevant features to achieve increased transparency in the guidance process. We report the results of an empirical user study with 120 participants that compares the integrated system to a system in which the filtering and advisory mechanisms operate separately. The findings indicate that displaying recommendations and explanations directly in the filter component can increase acceptance and trust in the system. Similarly, combining the advisor with the filters along with the displayed explanations leads to significantly higher levels of knowledge about the relevant product features.}, address = {Cham}, author = {Kleemann, Timm and Ziegler, Jürgen}, series = {Lecture Notes in Computer Science}, booktitle = {Human-Computer Interaction – INTERACT 2023 : 19th IFIP TC13 International Conference, York, UK, August 28 – September 1, 2023, Proceedings, Part III}, editor = {Abdelnour Nocera, José and Kristı́n Lárusdóttir, Marta and Petrie, Helen and Piccinno, Antonio and Winckler, Marco}, isbn = {9783031422850}, issn = {0302-9743}, volume = {14144}, doi = {10.1007/978-3-031-42286-7_8}, pages = {137–159}, publisher = {Springer Nature Switzerland}, title = {Blending Conversational Product Advisors and Faceted Filtering in a Graph-Based Approach}, url = {https://doi.org/10.1007/978-3-031-42286-7_8}, note = {10.1007/978-3-031-42286-7_8}, language = {en}, keywords = {Search interfaces; explanations; knowledge graph}, year = {2023}, month = {aug}, day = {25}, month_numeric = {8} } @inproceedings{ubo_mods_00199546, author = {Ma, Yuan and Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen}, editor = {Gwizdka, Jacek and Rieh, Soo Young}, title = {An Instrument for measuring users’ meta-intents}, booktitle = {CHIIR ’23: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval}, year = {2023}, publisher = {ACM}, address = {Washington}, pages = {290–302}, abstract = {We propose the concept of meta-intents which represent high-level user preferences related to the interaction and decision-making in conversational recommender systems (CRS) and present a questionnaire instrument for measuring meta-intents. We conducted a two-stage user study, an exploratory study with 212 participants on Prolific, and a confirmatory study with 394 participants on Prolific. We obtained a reliable and stable meta-intents questionnaire with 22 question items, corresponding to seven latent factors (concepts). These seven factors cover important interaction preferences and are closely related to users’ decision-making process. For example, the factor dialog-initiative reflects whether users prefer to follow the system’s guidance or ask their own questions in a CRS. We conducted statistical analyses of meta-intents in two domains (smartphones and hotels), and a general chatbot scenario. We also investigated the influence of additional factors (demography, decision-making style) on meta-intents through Structural Equation Modeling (SEM). Our results provide preliminary evidence that the proposed meta-intents are domain and demography (gender, age) independent. They can be linked to the general decision-making style and can thus be instrumental in translating general decision-making factors into more concrete design guidance for CRS and their potential personalization. Meta-intents also provide a basis for future analyses of interaction behavior in CRS and the development of a cognitively founded theoretical framework.}, isbn = {979-8-4007-0035-4}, doi = {10.1145/3576840}, url = {https://doi.org/10.1145/3576840.3578317}, language = {en} } @article{ubo_mods_00197856, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, title = {Creating Omni-Channel In-Store Shopping Experiences through Augmented-Reality-Based Product Recommending and Comparison}, journal = {International Journal of Human–Computer Interaction}, year = {2023}, publisher = {Taylor & Francis}, address = {New York}, pages = {in press}, note = {in press}, issn = {1044-7318}, doi = {10.1080/10447318.2022.2163650}, url = {https://doi.org/10.1080/10447318.2022.2163650}, language = {en} } @article{ubo_mods_00196239, author = {Kunkel, Johannes and Ziegler, Jürgen}, title = {A comparative study of item space visualizations for recommender systems}, journal = {International Journal of Human Computer Studies}, year = {2023}, publisher = {Elsevier}, address = {Amsterdam}, volume = {172}, keywords = {Empirical user studies; Information visualization; Maps; Recommender systems; Treemaps; User experience}, issn = {1095-9300}, doi = {10.1016/j.ijhcs.2022.102987}, url = {https://doi.org/10.1016/j.ijhcs.2022.102987}, language = {en} } @phdthesis{ubo_mods_00198640, author = {Kunkel, Johannes}, title = {Mental Models, Explanations, Visualizations: Promoting User-Centered Qualities in Recommender Systems}, year = {2022}, address = {Duisburg, Essen}, keywords = {Recommender Systems}, abstract = {Recommender systems (RSs) are powerful tools that proactively suggest a set of personalized items to users. In doing so, they aim to predict the preferences of their users, wherein they are considered to be very accurate. In addition to algorithmic precision, user-centered qualities have recently been increasingly taken into account when evaluating the success of RSs. Examples for such qualities include the transparency of an RS, the control users are able to exert over their recommendations, and the means of exploring the item space in context of recommendations. However, research on aspects focused on human-computer interaction in RSs is still at a rather early stage. The main focus of the present thesis is to study and design RSs more holistically. In this regard, the mental models that users create of RSs are explored, explanations and their impact on user-centered variables of RSs are investigated, and techniques from information visualization (InfoVis) are applied to let users scrutinize the global context of their recommendations. The results of this research and the contributions I make to the state of the art in this context are described in greater detail below. A key contribution of this thesis consists of the results of two studies that shed light on the mental models that users of RSs develop and how these models influence the users’ perception of different system qualities. A key finding of the first, qualitative study is that many mental models tend to follow a procedural structure that can be used, for instance, as a template for designing explanations to promote transparency in RSs. In the second study, which relied on a larger sample and thus allowed quantitative conclusions, this type of procedurally structured mental models was found to correlate with a high perception of system transparency and confidence in the users’ own comprehension of the inner workings of the system. Apart from that, some users seemed to humanize the RS, assigning attributes such as “social”, “organic”, and “empathic”. Such a comprehension of the system was accompanied by higher levels of trust—a finding that may be leveraged by system designers. In general, mental models that deviate greatly from the actual functioning of the system should be corrected so that they do not lead to false expectations on the part of the users and hence to a potentially rejection of recommendations. A prominent method for improving system transparency and thus the soundness of users’ mental models is to provide textual explanations along with the recommendations. These explanations usually follow a very simple scheme based on similarity—especially in productive environments. To investigate implications of such simple explanations, another experiment contained in this thesis asked users to explain recommendations in their own words and compared them to explanations automatically generated by a system. The results indicate many benefits of providing more extensive explanations for recommendations, such as increased trust and higher perceived quality of recommendations. Another finding is that many participants, as opposed to the system, provided a broader context of the decision behind their recommendation. The extent to which textual explanations can provide context for recommendations is limited,though. While a local context is relatively easy to explain textually—e.g. by linking recommendations to a user’s preferences—it is difficult, if not impossible, to provide users with a global context. Such a global context would need to explain the relationship of recommendations to all other items in the dataset from which a RS selects its candidates. Comprehending such an item space at a global scale can unlock several beneficial properties of an RS, such as preventing filter bubbles, fostering creativity, and encouraging a user’s self-development. In this thesis, I argue that to provide such a global context, RSs should go beyond explaining recommendations textually and better exploit the capabilities of computer systems compared to humans. Three of the six papers included in this cumulative dissertation explore how methods of InfoVis can be applied to RSs to provide users with a global context of recommendations and how this affects the users’ perception of these systems. One result of these studies is that even simple means of representing the item space can already successfully convey a sense of overview over the item space and provide transparency for recommendations. However, another finding is that artificial maps that distribute all items on a two-dimensional plane according to their similarity are a promising visualization style that can be used to deeply integrate means of interactively controlling recommendations into the visualization of the item space. Such maps have also been found to trigger user excitement, which can also influence the perception of recommendations. In another experiment, we found that a treemap can also be used as a control panel for a RSs. The results of this experiment further underline that treemaps can effectively alert their users to potential biases or blind spots in their preference profile. In this thesis, I discuss such implications of the InfoVis method to depict the item space of RSs. Finally, in this thesis I take an elevated perspective on the findings of the papers contained and argue that researchers should consider user-centered aspects of RSs more holistically, for instance, in terms of the deep interconnectedness of perceptual variables. In this sense, I observed that the user experience of an application can influence as how novel recommendations are perceived to be, and that the degree of overview of the item space users are able to obtain can positively affect the perceived quality of recommendations. This thesis represents thus a further argument for looking at RSs from a highly user-centered viewpoint.}, school = {University of Duisburg-Essen}, doi = {10.17185/duepublico/78167}, url = {https://doi.org/10.17185/duepublico/78167}, language = {en} } @inproceedings{ubo_mods_00191812, author = {Torkamaan, Helma and Ziegler, Jürgen}, title = {Recommendations as Challenges: Estimating Required Effort and User Ability for Health Behavior Change Recommendations}, booktitle = {27th International Conference on Intelligent User Interfaces}, series = {ACM Conferences}, year = {2022}, publisher = {Association for Computing Machinery}, address = {New York,NY,United States}, pages = {106–119}, keywords = {Ability; Behavior change; Difficulty; Elo; Glicko-2; Health recommender systems; Personalization; Rasch; TrueSkill}, isbn = {9781450391443}, doi = {10.1145/3490099}, url = {https://doi.org/10.1145/3490099.3511118}, language = {en} } @inproceedings{ubo_mods_00191514, author = {Loepp, Benedikt}, title = {Recommender Systems Alone Are Not Everything: Towards a Broader Perspective in the Evaluation of Recommender Systems}, booktitle = {PERSPECTIVES ’22: Proceedings of the 2nd Workshop on Perspectives on the Evaluation of Recommender Systems}, year = {2022}, abstract = {Thus far, in most of the user experiments conducted in the area of recommender systems, the respective system is considered as an isolated component, i.e., participants can only interact with the recommender that is under investigation. This fails to recognize the situation of users in real-world settings, where the recommender usually represents only one part of a greater system, with many other options for users to find suitable items than using the mechanisms that are part of the recommender, e.g., liking, rating, or critiquing. For example, in current web applications, users can often choose from a wide range of decision aids, from text-based search over faceted filtering to intelligent conversational agents. This variety of methods, which may equally support users in their decision making, raises the question of whether the current practice in recommender evaluation is sufficient to fully capture the user experience. In this position paper, we discuss the need to take a broader perspective in future evaluations of recommender systems, and raise awareness for evaluation methods which we think may help to achieve this goal, but have not yet gained the attention they deserve.}, url = {http://ceur-ws.org/Vol-3228/paper5.pdf} } @inproceedings{ubo_mods_00191230, author = {Ma, Yuan and Kleemann, Timm and Ziegler, Jürgen}, editor = {}, title = {Psychological User Characteristics and Meta-Intents in a Conversational Product Advisor}, booktitle = {Proceedings of the 9th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems}, series = {CEUR Workshop Proceedings}, year = {2022}, publisher = {}, address = {}, volume = {3222}, pages = {18–32}, keywords = {conversational UI design, interactive behavior analysis, decision making, influence of psychological factors on interaction}, abstract = {We present a study investigating psychological characteristics of users of a GUI-style conversational recommender system in a real-world application case. We collected data of 496 customers of an online shop using a conversational product advisor (CPA), using questionnaire responses concerning decision- making style and a set of meta-intents, a concept we propose to represent high-level user preferences related to the decision process in a CPA. We also analyzed anonymized data on users’ interactions in the CPA. Concerning general decision-making style, we could identify two clusters of users who differ in their scores on scales measuring rational and intuitive decision-making. We found evidence that rationality and intuitiveness scores are differently correlated with the proposed meta-intents such as efficiency orientation, interest in detail, and openness for guidance. Relations with interaction data could be observed between rationality/intuitiveness scores and overall time spent in the CPA. Trying to classify users’ decision style from their interactions, however did not yield positive results. Despite the limitation that only a single CPA was studied in a single domain, our results provide evidence that the proposed meta-intents are linked to the general decision-making style of a user and can thus be instrumental in translating general decision-making factors into more concrete design guidance for CPA and their potential personalization.}, issn = {1613-0073}, url = {http://ceur-ws.org/Vol-3222/paper2.pdf}, language = {en} } @inproceedings{ubo_mods_00185435, author = {Kleemann, Timm and Loepp, Benedikt and Ziegler, Jürgen}, publisher = {ACM}, address = {New York, NY, USA}, title = {Towards Multi-Method Support for Product Search and Recommending}, booktitle = {Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’22)}, year = {2022}, pages = {74–79}, keywords = {Decision aids; Faceted filtering; Conversational recommender systems; Product advisors; Chatbots; Recommender systems; Search interfaces}, isbn = {978-1-4503-9232-7}, doi = {10.1145/3511047}, url = {https://dl.acm.org/doi/10.1145/3511047.3536408?cid=87958660357}, abstract = {Today, online shops offer a variety of components to support users in finding suitable items, ranging from filters and recommendations to conversational advisors and natural language chatbots. All these methods differ in terms of cognitive load and interaction effort, and, in particular, in their suitability for the specific user. However, it is often difficult for users to determine which method to use to reach their goal. Moreover, as the settings are not propagated between the methods, there is a lack of support for switching components. In this paper, we study the reasons for using the different components in more detail and present an initial proposal for a multi-method approach that provides a more seamless experience, allowing users to freely and flexibly choose from all available methods at any time.} } @phdthesis{ubo_mods_00184619, author = {Torkamaan, Helma}, title = {Health Recommender Systems for Mental Health Promotion}, year = {2022}, address = {Duisburg, Essen}, keywords = {Health recommender systems; Mobile health; Mental health promotion; Mood; Stress; Mood Tracking}, abstract = {Recommender systems are today an essential part of software applications used in everyday life and facilitate the decision-making process for users by personalizing the options from which they can choose. An emerging and rapidly growing application domain for these systems is health care, and the majority of research contributions related to health recommender systems are about preventive health care. However, some crucial areas in this domain have been mostly overlooked. One such area is mental health promotion, which, despite its critical importance, has a relatively negligible share in existing solutions and research. User stress and mood are fundamental concepts in preventive health care, and proper skills for coping with stress and improving mood are crucial for individual mental well-being, which is the central theme of this dissertation. Health recommender systems have high potential benefits in personalizing health-related recommendations and, especially, engaging users in behavior change processes. A health recommender system for health promotion and behavior change is a holistic system that, ideally, uses techniques from ubiquitous computing to provide pervasive health. Building a health recommender system, therefore, is a multidisciplinary effort that engages various areas, which we summarize as tracking, interacting, and personalizing components, and address them in this dissertation regarding our recommendation domain, stress reduction. In particular, we discuss three major contributions to the problem of building health recommender systems for stress reduction and mood improvement: (1) establishing proper ways to track user mood; (2) building one of the first interactive mobile health recommender system research platforms and providing an extensive holistic dataset for flexible investigation of health recommender systems in the future; and (3) developing dynamic mood and health-aware, user-engaging algorithms and carefully comparing the performance and characteristics of the presented techniques. These contributions were the result of various mixed and longitudinal user studies which engaged with more than 2,500 users. This dissertation brings together for the first time various aspects of user decision-making - such as explicit short-term preferences, health needs, and long-term goals - for a holistic health-aware recommender system. By thoroughly discussing various components, this dissertation presents a roadmap for building health recommender systems, and interactive, mood-aware, and mental health-promoting systems in the future.}, school = {University of Duisburg-Essen}, doi = {10.17185/duepublico/76050}, url = {https://doi.org/10.17185/duepublico/76050} } @inproceedings{ubo_mods_00181315, author = {Hellmann, Marco and Hernandez Bocanegra, Diana and Ziegler, Jürgen}, editor = {Smith-Renner, Alison and Amir, Ofra}, title = {Development of an Instrument for Measuring Users’ Perception of Transparency in Recommender Systems}, booktitle = {Workshops at the International Conference on Intelligent User Interfaces (IUI) 2022: Proceedings of the IUI 2022 Workshops: APEx-UI, HAI-GEN, HEALTHI, HUMANIZE, TExSS, SOCIALIZE}, series = {CEUR Workshop Proceedings}, year = {2022}, publisher = {RWTH Aachen}, address = {Aachen}, volume = {3124}, pages = {156–165}, keywords = {Recommender systems}, abstract = {Transparency is increasingly seen as a critical requirement for achieving the goal of human-centered AI systems in general and also, specifically, recommender systems (RS). However, defining and operationalizing the concept is still difficult, due to its multi-faceted nature. Currently, there are hardly any measurement instruments to adequately assess the perceived transparency of RS in user studies. Thus, we present the development of a measurement instrument that aims at capturing perceived transparency as a multidimensional construct. The results of our validation show that transparency can be distinguished with respect to input (what data does the system use?), functionality (how and why is an item recommended?), output (why and how well does an item fit one’s preferences?), and interaction (what needs to be changed for a different prediction?). The study is intended as a first iteration in the development of a reliable and fully validated measurement tool for assessing transparency in RS.}, issn = {1613-0073}, doi = {10.17185/duepublico/75905}, url = {https://doi.org/10.17185/duepublico/75905}, language = {en} } @phdthesis{ubo_mods_00181111, author = {Hernandez Bocanegra, Diana Carolina}, title = {Argumentative Explanations for Recommendations Based on Reviews}, year = {2022}, address = {Duisburg, Essen}, keywords = {Recommender systems, Explanations, Argumentation, Interactive interfaces design, Conversational agent, Dataset, Empirical studies}, abstract = {Recommender systems (RS) assist users in making decisions on a wide range of tasks, while preventing them from being overwhelmed by enormous amounts of choices. RS prevalence is such that many users of information-based technologies interact with them on a daily basis. However, many of these systems are still perceived as black boxes by users, who often have no way of seeing or requesting the reasons why certain items are recommended, potentially leading to negative attitudes towards RS by users. Providing explanations in RS can bring several advantages for users’ decision making and overall user experience. Although different explanatory approaches have been proposed so far, the general lack of user evaluation, and validation of concepts and implementations of explainable methods in RS, have left open many questions, related to how such explanations should be structured and presented. Also, while explanations in RS have so far been presented mostly in a static and non-interactive manner, limited work in explainable artificial intelligence have emerged addressing interactive explanations, enabling users to examine in detail system decisions. However, little is known about how interactive interfaces in RS should be conceptualized and designed, so that explanatory aims such as transparency and trust are met. This dissertation investigates interactive, conversational explanations that enable users to freely explore explanatory content at will. Our work is grounded on RS explainable methods that exploit user reviews, and inspired by dialog models and formal argument structures. Following a user-centered approach, this dissertation proposes an interface design for explanations as interactive argumentation, which was empirically validated through different user studies. To this end, we implemented a RS able to provide explanations both through a graphical user interface (GUI) navigation and a natural language interface. The latter consists of a conversational agent for explainable RS, which supports conversation flows for different types of questions written by users in their own words. To this end, we formulated a model to facilitate the detection of the intent expressed by a user on a question, and collected and annotated a dataset helpful for intent detection, which can facilitate the development of explanatory dialog systems in RS. The results reported in this dissertation indicate that providing interactive explanations through a conversation, i.e. an exchange of questions and answers between the user and the system, using both GUI-navigation or natural language conversation, can positively impact users evaluation of explanation quality and of the system, in terms of explanatory aims like transparency, and trust.}, school = {University of Duisburg-Essen}, doi = {10.17185/duepublico/75833}, url = {https://doi.org/10.17185/duepublico/75833} } @inproceedings{ubo_mods_00170346, author = {Elahi, Mehdi and Abdollahpouri, Himan and Mansoury, Masoud and Torkamaan, Helma}, title = {Beyond Algorithmic Fairness in Recommender Systems}, booktitle = {UMAP ’21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization}, year = {2021}, publisher = {Association for Computing Machinery}, pages = {41–46}, keywords = {evaluation; fairness; recommender systems}, isbn = {978-1-4503-8367-7}, doi = {10.1145/3450614.3461685}, url = {https://doi.org/10.1145/3450614.3461685}, language = {en} } @phdthesis{ubo_mods_00169467, author = {Naveed, Sidra}, title = {An Interactive Hybrid Approach to Generate Explainable and Controllable Recommendations}, year = {2021}, address = {Duisburg, Essen}, keywords = {Collaborative Filtering}, abstract = {Recently, the increasing complexity of Recommender Systems (RS) algorithms has led RS researchers to focus on improving user-centric aspects beyond algorithmic accuracy. In this context, explanations and opportunities for user control have been shown to increase overall system effectiveness and acceptance among RS users. In the RS literature, many attempts have been made to explain recommendations, mostly by exploiting ratings given by the user community for products or other objects (generally:items), in the context of Collaborative Filtering (CF), or by exploiting product features in Content-Based Filtering (CB). While these methods may suffice in simple recommendation scenarios, the complexity of other situations may require the use of several different data sources, for example depending on the product domain. To this end, hybrid systems combining the advantages of CF and CB techniques have been developed, but mainly to improve the algorithmic performance of RS. However, the relationship between recommended items and user preferences for product features has not been sufficiently exploited to generate explainable recommendations. Moreover, in most cases, item rating or re-rating is the only way for users to indicate their preferences and control the recommendation process. This thesis assumes that exploiting users’ feature preferences together with item ratings in a CF approach can generate more explainable recommendations and provide users with better opportunities to control the recommendation process which can be especially beneficial in complex domains such as digital cameras. From this starting point, this thesis first introduces the Feature-based Collaborative Filtering approach in the domain of digital cameras, which extends the conventional CF method by exploiting the feature preferences of similar users instead of item preferences with the goal of generating explainable recommendations. The first prototype implemented based on the approach was then empirically evaluated in two user studies. The results show that our novel approach is superior to conventional item-based CF in terms of subjective assessment of explanations, while a pure CB approach was perceived similarly positively. However, overall, participants appreciated the availability of extended explanations compared to conventional recommender approaches. Based on the findings of the user studies, we developed an extended version of the approach and implemented a prototype called Featuristic, in which we integrated interactive mechanisms into three main phases of the recommendation process: 1) Preference Elicitation, 2) Recommendations, and 3) Explanations – to improve explanation quality and user control. The Featuristic prototype was then evaluated in two user studies. The results showed that the implemented interactive mechanisms in several components of the recommendation process overall improved the explainability and controllability of RS compared to systems offering only non-interactive recommendations with limited or no explanations at all.}, school = {University of Duisburg-Essen}, doi = {10.17185/duepublico/75001}, url = {https://doi.org/10.17185/duepublico/75001} } @inproceedings{ubo_mods_00168133, author = {Torkamaan, Helma and Ziegler, Jürgen}, title = {Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender System}, booktitle = {UbiComp ’21: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers}, year = {2021}, publisher = {Association for Computing Machinery}, address = {New York}, pages = {218–225}, keywords = {Behavior Change; Health recommender systems; Persuasive Design}, isbn = {978-1-4503-8461-2}, doi = {10.1145/3460418}, url = {https://doi.org/10.1145/3460418.3479330}, language = {en} } @inproceedings{ubo_mods_00168064, author = {Hernandez-Bocanegra, Diana Carolina and Ziegler, Jürgen}, title = {ConvEx-DS: A Dataset for Conversational Explanations in Recommender Systems}, booktitle = {Interfaces and Human Decision Making for Recommender Systems 2021: Proceedings of the 8th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems}, series = {CEUR Workshop Proceedings}, year = {2021}, publisher = {CEUR-WS}, address = {Aachen}, volume = {2948}, pages = {3–20}, keywords = {Conversational agent; Dataset; Explanations; Recommender systems; User study}, issn = {1613-0073}, language = {en} } @inproceedings{ubo_mods_00168051, author = {Donkers, Tim and Ziegler, Jürgen}, title = {The Dual Echo Chamber: Modeling Social Media Polarization for Interventional Recommending}, booktitle = {Fifteenth ACM Conference on Recommender Systems}, year = {2021}, publisher = {Association for Computing Machinery, Inc}, address = {New York}, pages = {12–22}, keywords = {Agent-based modeling; Knowledge graphs; Machine learning; Recommender systems}, isbn = {9781450384582}, doi = {10.1145/3460231.3474261}, url = {https://doi.org/10.1145/3460231.3474261}, language = {en} } @inproceedings{ubo_mods_00167910, author = {Kunkel, Johannes and Ngo, Phuong Thao and Ziegler, Jürgen and Krämer, Nicole}, editor = {Ardito, Carmelo and Lanzilotti, Rosa and Malizia, Alessio and Petrie, Helen and Piccinno, Antonio and Desolda, Giuseppe and Inkpen, Kori}, title = {Identifying Group-Specific Mental Models of Recommender Systems: A Novel Quantitative Approach}, booktitle = {Human-Computer Interaction – INTERACT 2021: Proceedings, Part IV}, series = {Lecture Notes in Computer Science}, year = {2021}, publisher = {Springer}, address = {Cham}, volume = {12935}, pages = {383–404}, keywords = {Card sorting; Hierarchical clustering; Mental models; Recommender systems; Transparency}, isbn = {978-3-030-85609-0}, doi = {10.1007/978-3-030-85610-6_23}, url = {https://doi.org/10.1007/978-3-030-85610-6_23}, language = {en} } @inproceedings{ubo_mods_00167903, author = {Hernandez Bocanegra, Diana Carolina and Ziegler, Jürgen}, editor = {Ardito, Carmelo and Lanzilotti, Rosa and Malizia, Alessio and Petrie, Helen and Piccinno, Antonio and Desolda, Giuseppe and Inkpen, Kori}, title = {Effects of Interactivity and Presentation on Review-Based Explanations for Recommendations}, booktitle = {Human-Computer Interaction – INTERACT 2021: Proceedings, Part II}, series = {Lecture Notes in Computer Science}, year = {2021}, publisher = {Springer}, address = {Cham}, volume = {12933}, pages = {597–618}, keywords = {Explanations; Interactivity; Recommender systems; User characteristics; User study}, abstract = {User reviews have become an important source for recommending and explaining products or services. Particularly, providing explanations based on user reviews may improve users’ perception of a recommender system (RS). However, little is known about how review-based explanations can be effectively and efficiently presented to users of RS. We investigate the potential of interactive explanations in review-based RS in the domain of hotels, and propose an explanation scheme inspired by dialogue models and formal argument structures. Additionally, we also address the combined effect of interactivity and different presentation styles (i.e. using only text, a bar chart or a table), as well as the influence that different user characteristics might have on users’ perception of the system and its explanations. To such effect, we implemented a review-based RS using a matrix factorization explanatory method, and conducted a user study. Our results show that providing more interactive explanations in review-based RS has a significant positive influence on the perception of explanation quality, effectiveness and trust in the system by users, and that user characteristics such as rational decision-making style and social awareness also have a significant influence on this perception.}, isbn = {978-3-030-85615-1}, doi = {10.1007/978-3-030-85616-8_35}, url = {https://doi.org/10.1007/978-3-030-85616-8_35}, language = {en} } @inproceedings{ubo_mods_00167803, author = {Ma, Yuan and Kleemann, Timm and Ziegler, Jürgen}, title = {Mixed-Modality Interaction in Conversational Recommender Systems}, booktitle = {Proceedings of the 8th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems}, series = {CEUR Workshop Proceedings}, year = {2021}, publisher = {}, address = {}, volume = {2948}, pages = {21–37}, keywords = {Conversational Recommender Systems; User Interface; Preference Elicitation; Critique-based Recommendations}, abstract = {Recent advances in natural language processing have made modern chatbots and Conversational Recommender Systems (CRS) increasingly intelligent, enabling them to handle more complex user inputs. Still, the interaction with a CRS is often tedious and error-prone. Especially when using written text as the form of conversation, the interaction is often less efficient in comparison to conventional GUI- style interaction. To keep the flexibility and mixed-initiative style of language-based conversation while leveraging the efficiency and simplicity of interacting through graphical widgets, we investigate the de- sign space of integrating GUI elements into text-based conversations. While simple response buttons have already been used in chatbots, the full range of such mixed-modality interactions has not yet been investigated in existing research. We propose two design dimensions along which integrations can be defined and analyze their applicability for preference elicitation and for critiquing the CRS’s responses at different levels. We report a user study in which we investigated user preferences and perceived usability of different techniques based on video prototypes.}, note = {OA platinum}, issn = {1613-0073}, url = {http://ceur-ws.org/Vol-2948/paper2.pdf}, language = {en} } @inproceedings{ubo_mods_00167689, author = {Kleemann, Timm and Wagner, Magdalena and Loepp, Benedikt and Ziegler, Jürgen}, title = {Modeling User Interaction at the Convergence of Filtering Mechanisms, Recommender Algorithms and Advisory Components}, booktitle = {Mensch Und Computer 2021 – Tagungsband}, year = {2021}, publisher = {ACM}, address = {New York, NY, USA}, pages = {531–543}, keywords = {Human factors; User experience; User modeling; Search interfaces; Recommender systems}, isbn = {978-1-4503-8645-6}, doi = {10.1145/3473856}, url = {https://dl.acm.org/doi/10.1145/3473856.3473859?cid=87958660357}, language = {en}, abstract = {A variety of methods is used nowadays to reduce the complexity of product search on e-commerce platforms, allowing users, for example, to specify exactly the features a product should have, but also, just to follow the recommendations automatically generated by the system. While such decision aids are popular with system providers, research to date has mostly focused on individual methods rather than their combination. To close this gap, we propose to support users in choosing the right method for the current situation. As a first step, we report in this paper a user study with a fictitious online shop in which users were able to flexibly use filter mechanisms, rely on recommendations, or follow the guidance of a dialog-based product advisor. We show that from the analysis of the interaction behavior, a model can be derived that allows predicting which of these decision aids is most useful depending on the user’s situation, and how this is affected by demographics and personality.} } @inproceedings{ubo_mods_00167688, author = {Loepp, Benedikt}, title = {On the Convergence of Intelligent Decision Aids}, booktitle = {Mensch Und Computer 2021 – Workshopband}, year = {2021}, publisher = {Gesellschaft für Informatik e.V.}, address = {Bonn}, keywords = {Decision support; Human factors; Information filtering; Adaptive systems; Recommender systems; User experience; User modeling}, abstract = {On the one hand, users’ decision making in today’s web is supported in numerous ways, with mechanisms ranging from manual search over automated recommendation to intelligent advisors. The focus on algorithmic accuracy, however, is questioned more and more. On the other hand, although the boundaries between the mechanisms are blurred increasingly, research on user-related aspects is still conducted separately in each area. In this position paper, we present a research agenda for providing a more holistic solution, in which users are supported with the right decision aid at the right time depending on personal characteristics and situational needs.}, doi = {10.18420/muc2021-mci-ws02-371}, url = {https://doi.org/10.18420/muc2021-mci-ws02-371}, language = {en} } @inproceedings{ubo_mods_00167304, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, editor = {Ardito, Carmelo and Lanzilotti, Rosa and Malizia, Alessio and Petrie, Helen and Piccinno, Antonio and Desolda, Giuseppe and Inkpen, Kori}, title = {Acceptance of an AR-Based In-Store Shopping Advisor: The Impact of Psychological User Characteristics}, booktitle = {Human-Computer Interaction – INTERACT 2021: Proceedings, Part I}, series = {Lecture Notes in Computer Science}, year = {2021}, publisher = {Springer}, address = {Berlin}, volume = {12932}, pages = {457–479}, keywords = {Technology acceptance; Augmented reality; Retailing; Shopping advisors}, isbn = {978-3-030-85622-9}, doi = {10.1007/978-3-030-85623-6_28}, url = {https://doi.org/10.1007/978-3-030-85623-6_28}, language = {en}, abstract = {We present a study on the acceptance of augmented reality-based product comparison and recommending in a physical store context. An online study was performed, in which a working prototype for head-mounted displays, developed in previous research, was used to showcase the concept. The survey included questionnaires to assess shopping behavior, decision styles and propensity to adopt new technologies of the participants. A cluster analysis of these psychological traits reveals the existence of different types of customers, who also differ on their assessment of the system. While the technology adoption propensity index is the better predictor of the acceptance of an augmented reality shopping advisor, the results suggest that factors such as the user’s previous experience, a high experiential chronic shopping orientation, or an intuitive decision style have a significant impact on it as well. Thus, predicting user acceptance solely based on one of the investigated psychological traits may be unreliable, and studying them in conjunction can provide a more accurate estimation.} } @inproceedings{ubo_mods_00167074, author = {Hernandez-Bocanegra, Diana C. and Ziegler, Jürgen}, title = {Conversational Review-based Explanations for Recommender Systems: Exploring Users’ Query Behavior}, booktitle = {CUI 2021 - 3rd Conference on Conversational User Interfaces}, series = {ACM International Conference Proceeding Series}, year = {2021}, publisher = {Association for Computing Machinery (ACM)}, address = {New York}, keywords = {argumentation; conversational agent; explanations; Recommender systems; user study}, abstract = {Providing explanations based on user reviews in recommender systems (RS) can increase users’ perception of system transparency. While static explanations are dominant, interactive explanatory approaches have emerged in explainable artificial intelligence (XAI), so that users are more likely to examine system decisions and get more arguments supporting system assertions. However, little attention has been paid to conversational approaches for explanations targeting end users. In this paper we explore how to design a conversational interface to provide explanations in a review-based RS, and present the results of a Wizard of Oz (WoOz) study that provided insights into the type of questions users might ask in such a context, as well as their perception of a system simulating such a dialog. Consequently, we propose a dialog management policy and user intents for explainable review-based RS, taking as an example the hotels domain.}, isbn = {9781450389983}, doi = {10.1145/3469595.3469596}, url = {https://dl.acm.org/doi/10.1145/3469595.3469596?cid=99659550942}, language = {en} } @inproceedings{ubo_mods_00166333, author = {Herrmanny, Katja and Torkamaan, Helma}, editor = {Masthoff, Judith and Herder, Eelco and Tintarev, Nava and Tkalčič, Marko}, title = {Towards a User Integration Framework for Personal Health Decision Support and Recommender Systems}, booktitle = {Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization}, year = {2021}, publisher = {Association for Computing Machinery}, address = {New York}, pages = {65–76}, keywords = {Decision support system; Design framwork; Health recommender systems; User in the loop; User integration}, isbn = {978-1-4503-8366-0}, doi = {10.1145/3450613.3456816}, url = {https://doi.org/10.1145/3450613.3456816}, language = {en} } @phdthesis{ubo_mods_00164523, author = {Loepp, Benedikt}, title = {Interactive Methods for Model-based Collaborative Filtering Recommender Systems}, year = {2021}, address = {Duisburg, Essen}, keywords = {Recommender systems; Collaborative filtering; Interactive recommending; Matrix factorization; Empirical studies; User experience; User interfaces}, abstract = {Recommender systems have become very popular for reducing the information overload users are often confronted with in today’s web. Collaborative filtering is the method of choice for generating personalized recommendations, supporting users in finding items that best match their preferences, from news articles and movies to all kinds of consumer goods and services. Model-based techniques have achieved great success in terms of recommendation accuracy and algorithmic performance. While there is a large body of research on these aspects, only little effort has been spent on improving user control and experience. As a consequence, users of contemporary systems usually have no other option than rating single items to indicate their preferences and thus to influence the recommendations. In this thesis, we propose a set of interactive methods for model-based collaborative filtering recommender systems. With these methods, we aim at providing users richer possibilities to specify their preferences and to control the outcome of the systems according to situational needs. In general, users should be enabled to take a more active role throughout the process of finding suitable items. Guided by a structured model of user interaction, we first present a choice-based preference elicitation method. For systems that rely on matrix factorization, one of the most commonly applied techniques in the area of model-based collaborative filtering, this method provides an alternative to rating items in cold-start situations. Furthermore, we describe an algorithmic enhancement, content-boosted matrix factorization. Based on the additional item-related information that is considered by this method, we give several examples of advanced interactive features that allow users to control the recommendations in an even more expressive manner, also later in the process. Finally, we propose a concept called blended recommending. This concept is designed to merge model-based collaborative filtering with other established methods in a way that users can be supported also in complex scenarios with the full range of options they need to reach their search goal. All these methodological contributions are complemented by empirical evaluations. Overall, we conducted four user experiments with n=35, 46, 54 and 33 participants, respectively. The results underline that our methods can effectively be implemented in existing recommender systems in order to turn them into fully interactive, user-controlled applications. This is finally confirmed with the help of an integrated recommendation platform that demonstrates that all our developments can be combined with each other in a single holistic system.}, school = {University of Duisburg-Essen}, doi = {10.17185/duepublico/74289}, url = {https://doi.org/10.17185/duepublico/74289} } @inproceedings{ubo_mods_00166665, author = {Hernandez Bocanegra, Diana Carolina and Borchert, Angela and Brünker, Felix and Shahi, Gautam Kishore and Ross, Björn}, title = {Towards a Better Understanding of Online Influence: Differences in Twitter Communication Between Companies and Influencers}, booktitle = {ACIS 2020 Proceedings}, series = {ACIS 2020 Proceedings}, year = {2020}, volume = {18}, keywords = {Online influence}, abstract = {In the last decade, Social Media platforms such as Twitter have gained importance in the various marketing strategies of companies. This work aims to examine the presence of influential content on a textual level, by investigating characteristics of tweets in the context of social impact theory, and its dimension immediacy. To this end, we analysed influential Twitter communication data during Black Friday 2018 with methods from social media analytics such as sentiment analysis and degree centrality. Results show significant differences in communication style between companies and influencers. Companies published longer textual content and created more tweets with a positive sentiment and more first-person pronouns than influencers. These findings shall serve as a basis for a future experimental study to examine the impact of text presence on consumer cognition and the willingness to purchase.}, url = {https://aisel.aisnet.org/acis2020/18/}, language = {en} } @inproceedings{ubo_mods_00166661, author = {Hernandez Bocanegra, Diana Carolina and Ziegler, Jürgen}, editor = {Hansen, C. and Nürnberger, A. and Preim, B.}, title = {Argumentative explanations for recommendations - Effect of display style and profile transparency}, booktitle = {Mensch und Computer 2020}, year = {2020}, keywords = {Recommender systems, explanations, user study}, abstract = {Providing explanations based on user reviews in recommender systems may increase users’ perception of transparency. However, little is known about how these explanations should be presented to users in order to increase both their understanding and acceptance. We present in this paper a user study to investigate the effect of different display styles (visual and text only) on the perception of review-based explanations for recommended hotels. Additionally, we also aim to test the differences in users’ perception when providing information about their own profiles, in addition to a summarized view on the opinions of other users about the recommended hotel. Our results suggest that the perception of explanations regarding these aspects may vary depending on user characteristics, such as decision-making styles or social awareness.}, doi = {10.18420/muc2020-ws111-338}, url = {https://doi.org/10.18420/muc2020-ws111-338}, language = {en} } @article{ubo_mods_00161373, author = {Hernandez-Bocanegra, Diana C. and Ziegler, Jürgen}, title = {Explaining Review-Based Recommendations: Effects of Profile Transparency, Presentation Style and User Characteristics}, journal = {i-com: Journal of Interactive Media}, year = {2020}, publisher = {de Gruyter}, address = {Berlin}, volume = {19}, number = {3}, pages = {181–200}, keywords = {user study}, abstract = {Providing explanations based on user reviews in recommender systems (RS) may increase users’ perception of transparency or effectiveness. However, little is known about how these explanations should be presented to users, or which types of user interface components should be included in explanations, in order to increase both their comprehensibility and acceptance. To investigate such matters, we conducted two experiments and evaluated the differences in users’ perception when providing information about their own profiles, in addition to a summarized view on the opinions of other customers about the recommended hotel. Additionally, we also aimed to test the effect of different display styles (bar chart and table) on the perception of review-based explanations for recommended hotels, as well as how useful users find different explanatory interface components. Our results suggest that the perception of an RS and its explanations given profile transparency and different presentation styles, may vary depending on individual differences on user characteristics, such as decision-making styles, social awareness, or visualization familiarity.}, issn = {2196-6826}, doi = {10.1515/icom-2020-0021} } @article{ubo_mods_00160743, author = {Koch, Michael and Ziegler, Jürgen and Reuter, Christian and Schlegel, Thomas and Prilla, Michael}, title = {Mensch-Computer-Interaktion als zentrales Gebiet der Informatik: Bestandsaufnahme, Trends und Herausforderungen}, journal = {Informatik Spektrum}, year = {2020}, publisher = {Springer}, address = {Berlin}, volume = {43}, pages = {381–387}, keywords = {Mensch-Computer-Interaktion}, issn = {1432-122X}, doi = {10.1007/s00287-020-01299-8} } @incollection{ubo_mods_00160728, author = {Ziegler, Jürgen and Loepp, Benedikt}, title = {Empfehlungssysteme}, booktitle = {Handbuch Digitale Wirtschaft}, year = {2020}, publisher = {Springer Gabler}, address = {Wiesbaden, Germany}, pages = {717–741}, keywords = {Empfehlungen; Personalisierung; E-Commerce; Recommender Systems; Collaborative Filtering; Usability}, isbn = {978-3-658-17290-9}, doi = {10.1007/978-3-658-17291-6_52}, url = {https://link.springer.com/chapter/10.1007%2F978-3-658-17291-6_52}, editor = {Kollmann, Tobias}, abstract = {Empfehlungssysteme stellen heute eine zentrale Komponente vieler Online-Plattformen dar, die bei Online-Shops und vielen anderen Anwendungen häufig zum Einsatz kommt. Ziel ist es, dem Kunden entsprechend seinen persönlichen Präferenzen Produkte oder andere Artikel vorzuschlagen, die für ihn von Interesse sind und potenziell zu einem Kauf oder generell zur Nutzung führen. Empfehlungssysteme haben eine erhebliche wirtschaftliche Bedeutung, da sie in vielen Fällen zu einem signifikanten Anteil zu Erfolgsfaktoren wie Click-through-Raten oder Käufen beitragen. Wir stellen in diesem Kapitel die unterschiedlichen Ansätze zur automatisierten Empfehlungsgebung vor und beschreiben konkrete Techniken zu deren Umsetzung. Weiterhin gehen wir auf wesentliche Aspekte der Gestaltung und Bewertung von Empfehlungssystemen ein und diskutieren anwenderrelevante Themen wie Usability und Vertrauen in systemgenerierte Empfehlungen.} } @inproceedings{ubo_mods_00157832, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, title = {In-Store Augmented Reality-Enabled Product Comparison and Recommendation}, booktitle = {14th ACM Conference on Recommender Systems}, year = {2020}, publisher = {Association for Computing Machinery (ACM)}, address = {New York}, pages = {180–189}, keywords = {recommender systems}, isbn = {9781450375832}, doi = {10.1145/3383313.3412266}, abstract = {We present an approach combining the AR-based presentation of product attributes in a physical retail store with recommendations for items only available online. The system supports users’ decision-making process by offering functions for comparing product features between items, both physical and online, and by providing recommendations based on selecting in-store products. The physical products may thus serve as anchors for forming the user’s preferences, also offering a richer and more engaging experience when exploring the products hands-on. Both objective product attributes as well as the visual appearance of a physical product are employed for generating recommendations from the online space. In this way, the advantages of online and in-store shopping can be combined, creating novel multi-channel opportunities for businesses. An empirical evaluation showed that the comparison and recommendation functions were appreciated by users, and hinted some possible benefits of a hybrid physical-online shopping support system. Despite the limitations of the study, there is sufficient evidence to consider this a viable approach worth to be further explored.} } @inproceedings{ubo_mods_00157986, author = {Naveed, Sidra and Ziegler, Jürgen}, title = {Featuristic: An interactive hybrid system for generating explainable recommendations – Beyond system accuracy}, booktitle = {Proceedings of the 7th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems}, year = {2020}, pages = {14–25}, keywords = {User Experience}, issn = {1613-0073}, url = {http://ceur-ws.org/Vol-2682/paper2.pdf}, abstract = {Hybrid recommender systems (RS) have shown to improve system accuracy by combining benefits from the collaborative filtering (CF) and content-based (CB) approaches. Recently, the increasing complexity of such algorithms has fueled a demand for researchers to focus more on the user-oriented aspects such as explainability, user interaction, and control mechanisms. Even in cases, where explanations are provided, the systems mostly fall short in explaining the connection between the recommended items and users? preferred features. Additionally, in most cases, rating or re-evaluating items is typically the only option for users to specify or manipulate their preferences. With the purpose to provide advanced explanations, we implemented a prototype system called Featuristic, by applying a hybrid approach that uses content-features in a CF approach and exploits feature-based similarities. Addressing important user-oriented aspects, we have integrated interactive mechanisms into the system to improve both preference elicitation and preference manipulation. Besides, we have integrated explanations for the recommendations into these interactive mechanisms. We evaluated our prototype system in two user studies to investigate the impact of the interactive explanations on the user-oriented aspects. The results showed that the Featuristic System with interactive explanations have significantly improved users’ perception of the system in terms of the preference elicitation, explainability, and preference manipulation – compared to the systems that provide non-interactive explanations.} } @inproceedings{Torkamaan_2020_exploring, author = {Torkamaan, Helma and Ziegler, Jürgen}, title = {Exploring chatbot user interfaces for mood measurement: A study of validity and user experience}, booktitle = {Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers}, year = {2020}, publisher = {Association for Computing Machinery (ACM)}, address = {New York}, pages = {135–138}, keywords = {PANAS}, abstract = {With the growth of interactive text or voice-enabled systems, such as intelligent personal assistants and chatbots, it is now possible to easily measure a user’s mood using a conversation-based interaction instead of traditional questionnaires. However, it is still unclear if such mood measurements would be valid, akin to traditional measures, and user-engaging. Using smartphones, we compare in this paper two of the most popular traditional measures of mood: International PANAS-Short Form (I-PANAS-SF) and Affect Grid. For each of these measures, we then investigate the validity of mood measurement with a modified, chatbot-based user interface design. Our preliminary results suggest that some mood measures may not be resilient to modifications and that their alteration could lead to invalid, if not meaningless results. This exploratory paper then presents and discusses four voice-based mood tracker designs and summarizes user perception of and satisfaction with these tools. \textcopyright 2020 Owner/Author.}, isbn = {9781450380768}, doi = {10.1145/3410530.3414395}, url = {https://dl.acm.org/doi/10.1145/3410530.3414395} } @inproceedings{ubo_mods_00156892, author = {Kleemann, Timm and Ziegler, Jürgen}, title = {Distribution sliders: Visualizing data distributions in range selection sliders}, booktitle = {Conference on "Mensch und Computer"}, series = {ACM International Conference Proceeding Series}, year = {2020}, publisher = {Association for Computing Machinery (ACM)}, address = {New York}, pages = {67–78}, isbn = {9781450375405}, doi = {10.1145/3404983.3405512}, abstract = {Sliders are often used to enable users to easily enter preferences for continuous data. Although efforts have already been made to enrich and improve these interaction tools with additional information and visualizations, only rather basic variants of sliders are commonly used in online shops or databases. However, these sliders often provide users only with very limited information about underlying data.We describe and evaluate three different slider designs, which enrich the tools with information in various ways, enabling users to efficiently explore the space of available items and to choose items in an informed manner. In one of the described slider designs we propose a new approach that integrates item recommendations directly into the slider, enabling users to see suitable items within the selection tool. In two user studies we show that these enhancements, both visualizations and recommendations, are powerful methods to directly support users in their searches.} } @article{ubo_mods_00154868, author = {Donkers, Tim and Ziegler, Jürgen}, title = {Leveraging Arguments in User Reviews for Generating and Explaining Recommendations}, journal = {Datenbank-Spektrum}, year = {2020}, publisher = {Springer}, address = {Berlin}, volume = {20}, number = {2}, pages = {181–187}, abstract = {Review texts constitute a valuable source for making system-generated recommendations both more accurate and more transparent. Reviews typically contain statements providing argumentative support for a given item rating that can be exploited to explain the recommended items in a personalized manner. We propose a novel method called Aspect-based Transparent Memories (ATM) to model user preferences with respect to relevant aspects and compare them to item properties to predict ratings, and, by the same mechanism, explain why an item is recommended. The ATM architecture consists of two neural memories that can be viewed as arrays of slots for storing information about users and items. The first memory component encodes representations of sentences composed by the target user while the second holds an equivalent representation for the target item based on statements of other users. An offline evaluation was performed with three datasets, showing advantages over two baselines, the well-established Matrix Factorization technique and a recent competitive representative of neural attentional recommender techniques.}, issn = {1618-2162}, doi = {10.1007/s13222-020-00350-y} } @inproceedings{ubo_mods_00154819, author = {Kunkel, Johannes and Schwenger, Claudia and Ziegler, Jürgen}, title = {NewsViz: Depicting and Controlling Preference Profiles Using Interactive Treemaps in News Recommender Systems}, booktitle = {UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization}, year = {2020}, publisher = {Association for Computing Machinery (ACM)}, address = {New York}, pages = {126–135}, keywords = {treemaps}, abstract = {News articles are increasingly consumed digitally and recommender systems (RS) are widely used to personalize news feeds for their users. Thereby, particular concerns about possible biases arise. When RS filter news articles opaquely, they might "trap" their users in filter bubbles. Additionally, user preferences change frequently in the domain of news, which is challenging for automated RS. We argue that both issues can be mitigated by depicting an interactive version of the user’s preference profile inside an overview of the entire domain of news articles. To this end, we introduce NewsViz, a RS that visualizes the domain space of online news as treemap, which can interactively be manipulated to personalize a feed of suggested news articles. In a user study (N=63), we compared NewsViz to an interface based on sliders. While both prototypes yielded high results in terms of transparency, recommendation quality and user satisfaction, NewsViz outperformed its counterpart in the perceived degree of control. Structural equation modeling allows us to further uncover hitherto underestimated influences between quality aspects of RS. For instance, we found that the degree of overview of the item domain influenced the perceived quality of recommendations.}, isbn = {9781450368612}, doi = {10.1145/3340631.3394869} } @inproceedings{ubo_mods_00154820, author = {Ngo, Thao Phuong and Kunkel, Johannes and Ziegler, Jürgen}, title = {Exploring Mental Models for Transparent and Controllable Recommender Systems: A Qualitative Study}, booktitle = {UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization}, year = {2020}, publisher = {Association for Computing Machinery (ACM)}, address = {New York}, pages = {183–191}, keywords = {transparent AI}, abstract = {While online content is personalized to an increasing degree, eg. using recommender systems (RS), the rationale behind personalization and how users can adjust it typically remains opaque. This was often observed to have negative effects on the user experience and perceived quality of RS. As a result, research increasingly has taken user-centric aspects such as transparency and control of a RS into account, when assessing its quality. However, we argue that too little of this research has investigated the users’ perception and understanding of RS in their entirety. In this paper, we explore the users’ mental models of RS. More specifically, we followed the qualitative grounded theory methodology and conducted 10 semi-structured face-to-face interviews with typical and regular Netflix users. During interviews participants expressed high levels of uncertainty and confusion about the RS in Netflix. Consequently, we found a broad range of different mental models. Nevertheless, we also identified a general structure underlying all of these models, consisting of four steps: data acquisition, inference of user profile, comparison of user profiles or items, and generation of recommendations. Based on our findings, we discuss implications to design more transparent, controllable, and user friendly RS in the future.}, isbn = {9781450368612}, doi = {10.1145/3340631.3394841} } @inproceedings{ubo_mods_00154785, author = {Naveed, Sidra and Loepp, Benedikt and Ziegler, Jürgen}, title = {On the Use of Feature-based Collaborative Explanations: An Empirical Comparison of Explanation Styles}, booktitle = {ExUM ’20: Proceedings of the International Workshop on Transparent Personalization Methods based on Heterogeneous Personal Data}, year = {2020}, publisher = {ACM}, address = {New York}, pages = {226–232}, keywords = {User Experience}, doi = {10.1145/3386392.3399303}, url = {https://dl.acm.org/doi/10.1145/3386392.3399303?cid=87958660357}, abstract = {Current attempts to explain recommendations mostly exploit a single type of data, i.e. usually either ratings provided by users for items in collaborative filtering systems, or item features in content-based systems. While this might be sufficient in straightforward recommendation scenarios, the complexity of other situations could require the use of multiple datasources, for instance, depending on the product domain. Even though hybrid systems have a long and successful history in recommender research, the connections between user ratings and item features have only rarely been used for offering more informative and transparent explanations. In previous work, we presented a prototype system based on a feature-weighting mechanism that constitutes an exception, allowing to recommend both items and features based on ratings while offering advanced explanations based on content data. In this paper, we empirically evaluate this prototype in terms of user-oriented aspects and user experience against to widely accepted baselines. Two user studies show that our novel approach outperforms conventional collaborative filtering, while a pure content-based system was perceived in a similarly positive light. Overall, the results draw a promising picture, which becomes particularly apparent from a user perspective when participants were specifically asked to use the explanations: they indicated in their qualitative feedback that they understood them and highly appreciated their availability.} } @inproceedings{ubo_mods_00154786, author = {Hernandez-Bocanegra, Diana C. and Donkers, Tim and Ziegler, Jürgen}, title = {Effects of Argumentative Explanation Types on the Perception of Review-Based Recommendations}, booktitle = {Adjunct Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’20 Adjunct)}, year = {2020}, publisher = {Association for Computing Machinery (ACM)}, address = {New York}, pages = {219–225}, keywords = {user study}, abstract = {Recommender systems have achieved considerable maturity and accuracy in recent years. However, the rationale behind recommendations mostly remains opaque. Providing textual explanations based on user reviews may increase users’ perception of transparency and, by that, overall system satisfaction. However, little is known about how these explanations can be effectively and efficiently presented to the user. In the following paper, we present an empirical study conducted in the domain of hotels to investigate the effect of different textual explanation types on, among others, perceived system transparency and trustworthiness, as well as the overall assessment of explanation quality. The explanations presented to participants follow an argument-based design, which we propose to provide a rationale to support a recommendation in a structured way. Our results show that people prefer explanations that include an aggregation using percentages of other users’ opinions, over explanations that only include a brief summary of opinions. The results additionally indicate that user characteristics such as social awareness may influence the perception of explanation quality.}, isbn = {9781450367110}, doi = {10.1145/3386392.3399302}, url = {https://dl.acm.org/doi/10.1145/3386392.3399302?cid=99659550942} } @inproceedings{ubo_mods_00148660, author = {Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen}, editor = {Paternò, Fabio and Oliver, Nuria}, title = {Explaining Recommendations by Means of Aspect-Based Transparent Memories}, booktitle = {Proceedings of the 25th International Conference on Intelligent User Interfaces}, year = {2020}, publisher = {The Association for Computing Machinery}, address = {New York, NY}, pages = {166–176}, isbn = {978-1-4503-7118-6}, doi = {10.1145/3377325.3377520}, url = {https://dl.acm.org/doi/pdf/10.1145/3377325.3377520}, abstract = {Recommender Systems have seen substantial progress in terms of algorithmic sophistication recently. Yet, the systems mostly act as black boxes and are limited in their capacity to explain why an item is recommended. In many cases recommendations methods are employed in scenarios where users not only rate items, but also convey their opinion on various relevant aspects, for instance by the means of textual reviews. Such user-generated content can serve as a useful source for deriving explanatory information to increase system intelligibility and, thereby, the user’s understanding. We propose a recommendation and explanation method that exploits the comprehensiveness of textual data to make the underlying criteria and mechanisms that lead to a recommendation more transparent. Concretely, the method incorporates neural memories that store aspect-related opinions extracted from raw review data. We apply attention mechanisms to transparently write and read information from memory slots. Besides customary offline experiments, we conducted an extensive user study. The results indicate that our approach achieves a higher overall quality of explanations compared to a state-of-the-art baseline. Utilizing Structural Equation Modeling, we additionally reveal three linked key factors that constitute explanation quality: Content adequacy, presentation adequacy, and linguistic adequacy.} } @inproceedings{ubo_mods_00144402, author = {Loepp, Benedikt and Ziegler, Jürgen}, title = {Measuring the Impact of Recommender Systems – A Position Paper on Item Consumption in User Studies}, booktitle = {Proceedings of the 1st Workshop on Impact of Recommender Systems (ImpactRS ’19)}, year = {2019}, keywords = {User Studies}, url = {https://impactrs19.github.io/papers/short4.pdf}, abstract = {While participants of recommender systems user studies usually cannot experience recommended items, it is common practice that researchers ask them to fill in questionnaires regarding the quality of systems and recommendations. While this has been shown to work well under certain circumstances, it sometimes seems not possible to assess user experience without enabling users to consume items, raising the question of whether the impact of recommender systems has always been measured adequately in past user studies. In this position paper, we aim at exploring this question by means of a literature review and at identifying aspects that need to be further investigated in terms of their influence on assessments in users studies, for instance, the difference between consumption of products or only of related information as well as the effect of domain, domain knowledge and other possibly confounding factors.} } @inproceedings{ubo_mods_00143261, author = {Ziegler, Jürgen}, editor = {Weyers, Benjamin and Bowen, Judy}, title = {Challenges in User-Centered Engineering of AI-based Interactive Systems}, booktitle = {Joint Proceedings HCI Engineering 2019 – Methods and Tools for Advanced Interactive Systems and Integration of Multiple Stakeholder Viewpoints co-located with 11th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2019)}, series = {CEUR Workshop Proceedings}, year = {2019}, address = {Aachen}, volume = {2503}, pages = {51–55}, keywords = {User Interface Engineering}, issn = {1613-0073}, url = {http://ceur-ws.org/Vol-2503/}, abstract = {Intelligent algorithms have reached a new level of performance in recent years and are increasingly employed in application areas such as speech and image recognition, data analytics, or recommender systems. The proliferation of these techniques poses a range of new challenges for the design and engineering of interactive systems since they tend to act as black boxes and do not offer the transparency and level of control to the user which is considered a prerequisite for user-centered design in the HCI field. In this position paper, we provide an overview of the broad areas related to intelligent algorithms and HCI that will need further research in the future to make systems useful, usable and trustable.} } @inproceedings{ubo_mods_00142455, author = {Loepp, Benedikt and Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen}, title = {Impact of Consuming Suggested Items on the Assessment of Recommendations in User Studies on Recommender Systems}, booktitle = {Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI ’19)}, year = {2019}, publisher = {IJCAI Organization}, pages = {6201–6205}, keywords = {Recommender Systems}, doi = {10.24963/ijcai.2019/863}, url = {https://doi.org/10.24963/ijcai.2019/863}, abstract = {User studies are increasingly considered important in research on recommender systems. Although participants typically cannot consume any of the recommended items, they are often asked to assess the quality of recommendations and of other aspects related to user experience by means of questionnaires. Not being able to listen to recommended songs or to watch suggested movies, might however limit the validity of the obtained results. Consequently, we have investigated the effect of consuming suggested items. In two user studies conducted in different domains, we showed that consumption may lead to differences in the assessment of recommendations and in questionnaire answers. Apparently, adequately measuring user experience is in some cases not possible without allowing users to consume items. On the other hand, participants sometimes seem to approximate the actual value of recommendations reasonably well depending on domain and provided information.} } @inproceedings{ubo_mods_00140448, author = {Loepp, Benedikt and Ziegler, Jürgen}, title = {Towards Interactive Recommending in Model-based Collaborative Filtering Systems}, booktitle = {Proceedings of the 13th ACM Conference on Recommender Systems (RecSys ’19)}, year = {2019}, publisher = {ACM}, address = {New York, NY, USA}, pages = {546–547}, keywords = {User Experience}, isbn = {978-1-4503-6243-6}, doi = {10.1145/3298689.3346949}, abstract = {Numerous attempts have been made for increasing the interactivity in recommender systems, but the features actually available in today’s systems are in most cases limited to rating or re-rating single items. We present a demonstrator that showcases how model-based collaborative filtering recommenders may be enhanced with advanced interaction and preference elicitation mechanisms in a holistic manner. Hereby, we underline that by employing methods we have proposed in the past it becomes possible to easily extend any matrix factorization recommender into a fully interactive, user-controlled system. By presenting and deploying our demonstrator, we aim at gathering further insights, both into how the different mechanisms may be intertwined even more closely, and how interaction behavior and resulting user experience are influenced when users can choose from these mechanisms at their own discretion.}, url = {https://dl.acm.org/doi/10.1145/3298689.3346949?cid=87958660357} } @inproceedings{ubo_mods_00140449, author = {Torkamaan, Helma and Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Bogers, Toine and Said, Alan}, title = {How Can They Know That? A Study of Factors Affecting the Creepiness of Recommendations}, booktitle = {Proceedings of the 13th ACM Conference on Recommender Systems}, year = {2019}, publisher = {ACM}, address = {New York, NY}, pages = {423–427}, keywords = {Trust}, isbn = {978-1-4503-6243-6}, doi = {10.1145/3298689.3346982}, abstract = {Recommender systems (RS) often use implicit user preferences extracted from behavioral and contextual data, in addition to traditional rating-based preference elicitation, to increase the quality and accuracy of personalized recommendations. However, these approaches may harm user experience by causing mixed emotions, such as fear, anxiety, surprise, discomfort, or creepiness. RS should consider users’ feelings, expectations, and reactions that result from being shown personalized recommendations. This paper investigates the creepiness of recommendations using an online experiment in three domains: movies, hotels, and health. We define the feeling of creepiness caused by recommendations and find out that it is already known to users of RS. We further find out that the perception of creepiness varies across domains and depends on recommendation features, like causal ambiguity and accuracy. By uncovering possible consequences of creepy recommendations, we also learn that creepiness can have a negative influence on brand and platform attitudes, purchase or consumption intention, user experience, and users’ expectations of—and their trust in—RS.} } @inproceedings{ubo_mods_00138306, author = {Kunkel, Johannes and Feldkamp, Tamara and Ziegler, Jürgen}, title = {Kartenbasierte Produktraumdarstellung zur Erhöhung von Transparenz und Steuerbarkeit in Empfehlungssystemen}, booktitle = {Mensch und Computer 2019: Tagungsband}, year = {2019}, publisher = {ACM}, address = {New York}, keywords = {Filterblasen}, note = {Poster Abstract}, doi = {10.1145/3340764.3344893}, abstract = {Empfehlungssysteme (ES) werden häufig eingesetzt, um Nutzer bei der Auswahl eines Produkts aus vielen Alternativen zu unterstützen. Während Empfehlungsalgorithmen hinsichtlich ihrer Präzision bereits sehr ausgereift sind, verhindern mangelnde Transparenz der Empfehlungen und fehlende Interaktionsmöglichkeiten, dass ES ihr volles Potential entfalten. In diesem Beitrag stellen wir eine Methode vor, die einerseits auf verständlichere Empfehlungen und mehr Kontrolle durch den Nutzern abzielt, andererseits aber auch dessen Übersicht über die Produktdomäne adressiert. Dabei dient eine Verteilung aller Produkte auf einer zweidimensionalen Fläche als Basis. Innerhalb können Nutzer ihre Präferenzen ausdrücken, woraufhin das ES mit passenden Empfehlungen reagiert. Um die Empfehlungen zu verändern, können Nutzer ihre Präferenzen anpassen, was in einem kontinuierlichen Feedback-Zyklus zwischen Nutzer und ES resultiert. Die Methode wird zudem an zwei Prototypen demonstriert, welche sie in verschiedenen Produktdomänen und mit unterschiedlichen Formen der Visualisierung und Interaktion umsetzen. Empirische Nutzerstudien zu den Prototypen versprechen ein hohes Potential des Ansatzes Übersicht, Transparenz und Kontrolle in ES zu verbessern.} } @inproceedings{ubo_mods_00139553, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, title = {Augmented-Reality-Enhanced Product Comparison in Physical Retailing}, booktitle = {Mensch und Computer 2019: Tagungsband}, year = {2019}, publisher = {ACM Press}, address = {New York}, pages = {55–65}, keywords = {natural interaction}, isbn = {978-1-4503-7198-8}, doi = {10.1145/3340764.3340800}, abstract = {Augmented reality technology has experienced great improvement in recent years and it has been successfully applied to industry and entertainment settings. However, its application in everyday contexts such as shopping is still very limited. One of the requirements to seamlessly incorporate augmented reality into everyday tasks is to find intuitive, natural methods to make use of it. Due to the inherent capabilities of augmented reality to work as a visual aid to explore and extend the knowledge a user has of the surroundings, this paper proposes the combination of AR technology and product advisors in a novel approach for product comparison. The user’s awareness of the differences between multiple physically present objects is enhanced through virtual augmentations, supporting an intuitive way of comparing two or more products while shopping. To assess the validity of the concept, a prototype for an AR-based shopping assistant for comparing vacuum cleaners has been implemented and evaluated in a user study, testing different methods of visual comparison and interaction.} } @inproceedings{ubo_mods_00139861, author = {Naveed, Sidra and Ziegler, Jürgen}, title = {Feature-driven interactive recommendations and explanations with collaborative filtering approach}, booktitle = {ComplexRec 2019: Proceedings of the Workshop on Recommendation in Complex Scenarios}, year = {2019}, volume = {2449}, pages = {10–15}, keywords = {Interactive recommendations}, url = {http://ceur-ws.org/Vol-2449/paper2.pdf} } @inproceedings{ubo_mods_00139865, author = {Millecamp, Martijn and Verbert, Katrien and Naveed, Sidra and Ziegler, Jürgen}, title = {To explain or not to explain: the effects of personal characteristics when explaining feature-based recommendations in different domains}, booktitle = {IntRS 2019: Proceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems}, year = {2019}, volume = {2450}, pages = {10–18}, keywords = {User modelling}, url = {http://ceur-ws.org/Vol-2450/paper2.pdf} } @inproceedings{ubo_mods_00138305, author = {Kunkel, Johannes and Loepp, Benedikt and Dolff, Esther and Ziegler, Jürgen}, title = {LittleMissFits: Ein Game-With-A-Purpose zur Evaluierung subjektiver Verständlichkeit von latenten Faktoren in Empfehlungssystemen}, booktitle = {Mensch und Computer 2019 – Workshopband}, year = {2019}, publisher = {Gesellschaft für Informatik e.V.}, pages = {49–56}, keywords = {Nutzerkontrolle}, doi = {10.18420/muc2019-ws-576}, url = {https://dx.doi.org/10.18420/muc2019-ws-576}, abstract = {Empfehlungssysteme, die mit Hilfe latenter Faktormodelle Empfehlungen generieren, arbeiten äußerst genau und sind entsprechend weit verbreitet. Da die Berechnung der Empfehlungen jedoch auf der statistischen Auswertung von Benutzerbewertungen basiert, gestaltet es sich schwierig, die Empfehlungen dem Nutzer gegenüber zu erklären. Daher werden die Systeme häufig als intransparent wahrgenommen und können oft ihr volles Potential nicht entfalten. Erste Ansätze zeigen allerdings, dass die latenten Faktoren solcher Modelle semantische Eigenschaften der Produkte widerspiegeln. Dabei ist bislang unklar, ob die zum Teil sehr komplexe Parametrisierung, die z.B. die Anzahl der Faktoren festlegt, Auswirkungen auf die semantische Verständlichkeit hat. Da dies sehr von der subjektiven Wahrnehmung abhängt, präsentieren wir mit LittleMissFits ein Online-Spiel, das es erlaubt, mittels Crowd-Sourcing die Konsistenz der latenten Faktoren zu untersuchen. Die Ergebnisse einer Nutzerstudie mit diesem Spiel zeigen, dass eine höhere Anzahl von Faktoren das Modell weniger verständlich erscheinen lässt. Darüber hinaus fanden sich Unterschiede innerhalb der Faktormodelle bezüglich der Verständlichkeit der einzelnen Faktoren. Zusammengenommen stellen die Ergebnisse eine wertvolle Grundlage dar, um künftig die Transparenz entsprechender Empfehlungssysteme zu steigern.} } @inproceedings{ubo_mods_00136811, author = {Kunkel, Johannes and Donkers, Tim and Michael, Lisa and Barbu, Catalin-Mihai and Ziegler, Jürgen}, title = {Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems}, booktitle = {Proceedings of the 37th International Conference on Human Factors in Computing Systems (CHI ’19)}, year = {2019}, publisher = {ACM}, address = {New York}, pages = {487:1–487:12}, isbn = {978-1-4503-5970-2}, doi = {10.1145/3290605.3300717}, url = {https://doi.org/10.1145/3290605.3300717}, abstract = {Trust in a Recommender System (RS) is crucial for its overall success. However, it remains underexplored whether users trust personal recommendation sources (i.e. other humans) more than impersonal sources (i.e. conventional RS), and, if they do, whether the perceived quality of explanation provided account for the difference. We conducted an empirical study in which we compared these two sources of recommendations and explanations. Human advisors were asked to explain movies they recommended in short texts while the RS created explanations based on item similarity. Our experiment comprised two rounds of recommending. Over both rounds the quality of explanations provided by users was assessed higher than the quality of the system’s explanations. Moreover, explanation quality significantly influenced perceived recommendation quality as well as trust in the recommendation source. Consequently, we suggest that RS should provide richer explanations in order to increase their perceived recommendation quality and trustworthiness.} } @inproceedings{ubo_mods_00132857, author = {Barbu, Catalin-Mihai and Carbonell, Guillermo and Ziegler, Jürgen}, title = {The Influence of Trust Cues on the Trustworthiness of Online Reviews for Recommendations}, booktitle = {Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing}, year = {2019}, publisher = {ACM Press}, address = {New York}, pages = {1687–1689}, keywords = {User study}, isbn = {978-1-4503-5933-7}, doi = {10.1145/3297280.3297603}, abstract = {In recent years, recommender systems have started to exploit user-generated content, in particular online reviews, as an additional means of personalizing and explaining their predictions. However, reviews that are poorly written or perceived as fake may have a detrimental effect on the users’ trust in the recommendations. Embedding so-called "trust cues" in the user interface is a technique that can help users judge the trustworthiness of presented information. We report preliminary results from an online user study that investigated the impact of trust cues—in the form of helpfulness votes—on the trustworthiness of online reviews for recommendations.} } @inproceedings{Kunkel.2019b, author = {Kunkel, Johannes and Ziegler, Jürgen}, title = {Visualizing Item Spaces to Increase Transparency and Control in Recommender Systems}, booktitle = {AI and HCI Workshop at CHI’19}, year = {2019} } @article{ubo_mods_00127145, author = {Carbonell, Guillermo and Barbu, Catalin-Mihai and Vorgerd, Laura and Brand, Matthias}, title = {The impact of emotionality and trust cues on the perceived trustworthiness of online reviews}, journal = {Cogent Business and Management}, year = {2019}, volume = {6}, number = {1}, pages = {1586062}, keywords = {trust cues}, abstract = {Online reviews and trust cues are two core aspects of e-commerce. Based on these features, users can make informed decisions about the products and services they buy online. Although prior studies have investigated on various review characteristics, the writing style has been examined less frequently. This empirical study simulated an e-commerce platform, in which participants (N =?124) were confronted with the reviews and helpfulness votes of other users while searching for one certain product (i.e. a laptop). The task was to rate how trustworthy or fake the reviews are, and the purchase intention after reading each review. Our results show that a factual writing style is considered more trustworthy, less fake, and entails a higher purchase intention when compared to emotional reviews. The trust cues were only relevant in interaction with variables that measure trust in the Internet as a safe environment for making monetary transactions. Furthermore, we found that trustworthiness influenced purchase intention, but the fakeness perception of the review does not yield such effects. We suggest future studies to understand this result and highlight implications for platform design.}, issn = {2331-1975}, doi = {10.1080/23311975.2019.1586062} } @article{ubo_mods_00109856, author = {Loepp, Benedikt and Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen}, volume = {121}, pages = {21–41}, title = {Interactive Recommending with Tag-Enhanced Matrix Factorization (TagMF)}, journal = {International Journal of Human Computer Studies}, year = {2019}, keywords = {Collaborative Filtering, Empirical studies, Human factors, Interactive recommending, Matrix Factorization, Recommender Systems, Tags, User experience, User interfaces, User profiles}, doi = {10.1016/j.ijhcs.2018.05.002}, url = {https://doi.org/10.1016/j.ijhcs.2018.05.002}, abstract = {We introduce TagMF, a model-based Collaborative Filtering method that aims at increasing transparency and offering richer interaction possibilities in current Recommender Systems. Model-based Collaborative Filtering is currently the most popular method that predominantly uses Matrix Factorization: This technique achieves high accuracy in recommending interesting items to individual users by learning latent factors from implicit feedback or ratings the community of users provided for the items. However, the model learned and the resulting recommendations can neither be explained, nor can users be enabled to influence the recommendation process except by rating (more) items. In TagMF, we enhance a latent factor model with additional content information, specifically tags users provided for the items. The main contributions of our method are to use this integrated model to elucidate the hidden semantics of the latent factors and to let users interactively control recommendations by changing the influence of the factors through easily comprehensible tags: Users can express their interests, interactively manipulate results, and critique recommended items—at cold-start when no historical data is yet available for a new user, as well as in case a long-term profile representing the current user’s preferences already exists. To validate our method, we performed offline experiments and conducted two empirical user studies where we compared a recommender that employs TagMF against two established baselines, standard Matrix Factorization based on ratings, and a purely tag-based interactive approach. This user-centric evaluation confirmed that enhancing a model-based method with additional information positively affects perceived recommendation quality. Moreover, recommendations were considered more transparent and users were more satisfied with their final choice. Overall, learning an integrated model and implementing the interactive features that become possible as an extension to contemporary systems with TagMF appears beneficial for the subjective assessment of several system aspects, the level of control users are able to exert over the recommendation process, as well as user experience in general.} } @inproceedings{ubo_mods_00117090, author = {Torkamaan, Helma and Ziegler, Jürgen}, title = {Multi-criteria rating-based preference elicitation in health recommender systems}, booktitle = {Proceedings of the Third International Workshop on Health Recommender Systems co-located with Twelfth ACM Conference on Recommender Systems (HealthRecSys’18)}, series = {CEUR Workshop Proceedings}, year = {2018}, month = {oct}, address = {Aachen}, volume = {2216}, pages = {18–23}, keywords = {Recommender systems}, issn = {1613-0073}, url = {http://ceur-ws.org/Vol-2216/healthRecSys18_paper_7.pdf}, venue = {Vancouver, BC, Canada}, month_numeric = {10} } @inproceedings{ubo_mods_00116566, author = {Loepp, Benedikt and Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen}, title = {Impact of Item Consumption on Assessment of Recommendations in User Studies}, booktitle = {Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18)}, year = {2018}, publisher = {ACM}, address = {New York, NY, USA}, pages = {49–53}, keywords = {User Studies}, isbn = {978-1-4503-5901-6}, doi = {10.1145/3240323.3240375}, url = {https://dl.acm.org/doi/10.1145/3240323.3240375?cid=87958660357}, abstract = {In user studies of recommender systems, participants typically cannot consume the recommended items. Still, they are asked to assess recommendation quality and other aspects related to user experience by means of questionnaires. Without having listened to recommended songs or watched suggested movies, however, this might be an error-prone task, possibly limiting validity of results obtained in these studies. In this paper, we investigate the effect of actually consuming the recommended items. We present two user studies conducted in different domains showing that in some cases, differences in the assessment of recommendations and in questionnaire results occur. Apparently, it is not always possible to adequately measure user experience without allowing users to consume items. On the other hand, depending on domain and provided information, participants sometimes seem to approximate the actual value of recommendations reasonably well.} } @inproceedings{ubo_mods_00116397, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, editor = {Dachselt, R. and Weber, G.}, title = {Augmented Reality Based Recommending in the Physical World}, booktitle = {Mensch und Computer 2018 - Workshopband}, year = {2018}, publisher = {Gesellschaft für Informatik e.V.}, address = {Bonn}, pages = {285–291}, keywords = {information visualization}, doi = {10.18420/muc2018-ws07-0461} } @inproceedings{ubo_mods_00116350, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Neidhardt, Julia and Wörndl, Wolfgang and Kuflik, Tsvi and Zanker, Markus}, title = {Designing Interactive Visualizations of Personalized Review Data for a Hotel Recommender System}, booktitle = {RecTour 2018: 3rd Workshop on Recommenders in Tourism co-located with the 12th ACM Conference on Recommender Systems (RecSys 2018)}, series = {CEUR Workshop Proceedings}, year = {2018}, publisher = {RWTH}, address = {Aachen}, volume = {2222}, pages = {7–12}, keywords = {Tourism}, abstract = {Online reviews extracted from social media are being used increasingly in recommender systems, typically to enhance prediction accuracy. A somewhat less studied avenue of research aims to investigate the underlying relationships that arise between users, items, and the topics mentioned in reviews. Identifying these–often implicit–relationships could be beneficial for at least a couple of reasons. First, they would allow recommender systems to personalize reviews based on a combination of both topic and user similarity. Second, they can facilitate the development of novel interactive visualizations that complement and help explain recommendations even further. In this paper, we report on our ongoing work to personalize user reviews and visualize them in an interactive manner, using hotel recommending as an example domain. We also discuss several possible interactive mechanisms and consider their potential benefits towards increasing users’ satisfaction.}, issn = {1613-0073}, url = {http://ceur-ws.org/Vol-2222/paper2.pdf} } @inproceedings{ubo_mods_00115757, author = {Loepp, Benedikt and Ziegler, Jürgen}, title = {Recommending Running Routes: Framework and Demonstrator}, booktitle = {Proceedings of the 2nd Second Workshop on Recommendation in Complex Scenarios (ComplexRec ’18)}, year = {2018}, pages = {26–29}, keywords = {Sports}, url = {http://toinebogers.com/workshops/complexrec2018/resources/proceedings.pdf#page=26}, abstract = {Recommending personalized running routes is a challenging task. When the runner’s specific background as well as needs, preferences and goals are taken into account, a recommender cannot only rely on e.g. a set of existing routes ran by others, but needs to individually generate each route while considering many different aspects that determine whether a suggestion will satisfy the runner in the end, e.g. height meters or areas passed. We describe a framework that summarizes these aspects, allowing to generate personalized running routes. Based on this framework, we present a prototypical smartphone app that we implemented to actually demonstrate how running routes can be recommended based on the different requirements a runner might have. A first small study where users had to try this app and ran some of the recommended routes underlines the general effectiveness of our approach.} } @inproceedings{ubo_mods_00115227, author = {Kunkel, Johannes and Loepp, Benedikt and Ziegler, Jürgen}, title = {Understanding Latent Factors Using a GWAP}, booktitle = {Proceedings of the Late-Breaking Results track part of the Twelfth ACM Conference on Recommender Systems (RecSys ’18)}, year = {2018}, keywords = {Game with a Purpose}, url = {https://arxiv.org/abs/1808.10260}, abstract = {Recommender systems relying on latent factor models often appear as black boxes to their users. Semantic descriptions for the factors might help to mitigate this problem. Achieving this automatically is, however, a non-straightforward task due to the models’ statistical nature. We present an output-agreement game that represents factors by means of sample items and motivates players to create such descriptions. A user study shows that the collected output actually reflects real-world characteristics of the factors.} } @inproceedings{ubo_mods_00114940, author = {Kunkel, Johannes and Loepp, Benedikt and Ziegler, Jürgen}, title = {Ein Online-Spiel zur Benennung latenter Faktoren in Empfehlungssystemen}, booktitle = {Mensch und Computer 2018 – Tagungsband}, year = {2018}, publisher = {Gesellschaft für Informatik e.V.}, keywords = {Games with a Purpose}, abstract = {Empfehlungssysteme, die auf latenten Faktormodellen basieren, sind dafür bekannt sehr genaue Vorschläge zu generieren. Häufig werden diese Systeme jedoch von Nutzern als intransparent wahrgenommen. Semantische Beschreibungen der latenten Faktoren könnten helfen, dieses Problem zu lindern. Solche Beschreibungen automatisch zu ermitteln gestaltet sich allerdings aufgrund der statistischen Herleitung der Faktoren aus numerischen Bewertungsdaten als schwierig. In diesem Beitrag stellen wir ein Output-Agreement-Spiel vor, das Spieler dazu motiviert, anhand repräsentativer Produkte Beschreibungen zu den Faktoren zu erstellen. Eine durchgeführte Nutzerstudie zeigt, dass das Spiel viel Spaß bereitet und die erhobenen Beschreibungen realweltliche Eigenschaften der Faktoren widerspiegeln.}, doi = {10.18420/muc2018-mci-0108}, url = {https://dl.gi.de/handle/20.500.12116/16729} } @inproceedings{ubo_mods_00114820, author = {Naveed, Sidra and Donkers, Tim and Ziegler, Jürgen}, title = {Argumentation-based explanations in recommender systems: Conceptual framework and empirical results}, booktitle = {UMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization}, year = {2018}, address = {New York, NY, USA}, publisher = {ACM}, pages = {293–298}, keywords = {User-centered}, isbn = {9781450357845}, doi = {10.1145/3213586.3225240} } @article{ubo_mods_00112246, author = {Taghavi, Mona and Bentahar, Jamal and Bakhtiyari, Kaveh and Hanachi, Chihab}, title = {New Insights Towards Developing Recommender Systems}, journal = {The Computer Journal}, year = {2018}, publisher = {Oxford University Press}, volume = {61}, number = {3}, pages = {319–348}, keywords = {taxonomy}, issn = {1460-2067}, doi = {10.1093/comjnl/bxx056} } @inproceedings{ubo_mods_00106122, author = {Kunkel, Johannes and Donkers, Tim and Barbu, Catalin-Mihai and Ziegler, Jürgen}, booktitle = {2nd Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE)}, title = {Trust-Related Effects of Expertise and Similarity Cues in Human-Generated Recommendations}, year = {2018}, keywords = {Structural Equation Modeling}, url = {http://ceur-ws.org/Vol-2068/humanize5.pdf}, abstract = {A user’s trust in recommendations plays a central role in the acceptance or rejection of a recommendation. One factor that influences trust is the source of the recommendations. In this paper we describe an empirical study that investigates the trust-related influence of social presence arising in two scenarios: human-generated recommendations and automated recommending. We further compare visual cues indicating the expertise of a human recommendation source and its similarity with the target user, and evaluate their influence on trust. Our analysis indicates that even subtle visual cues can signal expertise and similarity effectively, thus influencing a user’s trust in recommendations. These findings suggest that automated recommender systems could benefit from the inclusion of social components–especially when conveying characteristics of the recommendation source. Thus, more informative and persuasive recommendation interfaces may be designed using such a mixed approach.} } @inproceedings{ubo_mods_00104370, author = {Donkers, Tim and Loepp, Benedikt and Ziegler, Jürgen}, booktitle = {Proceedings of the 1st Workshop on Explainable Smart Systems (ExSS ’18)}, title = {Explaining Recommendations by Means of User Reviews}, year = {2018}, keywords = {Explanations}, url = {http://ceur-ws.org/Vol-2068/exss8.pdf}, abstract = {The field of recommender systems has seen substantial progress in recent years in terms of algorithmic sophistication and quality of recommendations as measured by standard accuracy metrics. Yet, the systems mainly act as black boxes for the user and are limited in their capability to explain why certain items are recommended. This is particularly true when using abstract models which do not easily lend themselves for providing explanations. In many cases, however, recommendation methods are employed in scenarios where users not only rate items, but also provide feedback in the form of tags or written product reviews. Such user-generated content can serve as a useful source for deriving explanatory information that may increase the user’s understanding of the underlying criteria and mechanisms that led to the results. In this paper, we describe a set of developments we undertook to couple such textual content with common recommender techniques. These developments have moved from integrating tags into collaborative filtering to employing topics and sentiments expressed in reviews to increase transparency and to give users more control over the recommendation process. Furthermore, we describe our current research goals and a first concept concerning extraction of more complex argumentative explanations from textual reviews and presenting them to users.} } @article{ubo_mods_00103875, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, title = {Negotiation and Reconciliation of Preferences in a Group Recommender System}, journal = {Journal of Information Processing}, year = {2018}, publisher = {Information Processing Society of Japan}, volume = {26}, pages = {186–200}, keywords = {decision-making}, issn = {1882-6652}, doi = {10.2197/ipsjjip.26.186}, abstract = {This article presents an approach to group recommender systems that focuses its attention on the group’s social interaction during the formulation, discussion and negotiation of the features the item to be jointly selected should possess. Current group recommender techniques are mainly based on aggregating existing user profiles or on a profile of the group as a whole. Our method supports collaborative preference elicitation and negotiation process where desired features have to be chosen individually, but group consensus is needed for them to become active in the item filtering process. Users provide feedback on the selected preferences and change their significance, bringing up new recommendations each time individual settings are modified. The last stage in the decision process is also supported, when users collectively select the final item from the recommendation set. We explored the possible benefits of our approach through the development of three prototypes, each based on a different variant of the approach with a different emphasis on private and group-wide preference spaces. They were evaluated with user groups of different size, addressing questions regarding the effectiveness of different information sharing methods and the repercussion of group size in the recommendation process. We compare the different methods and consolidate the findings in an initial model of recommending for group.} } @inproceedings{10.1007/978-3-319-53676-7_2, author = {Jannach, Dietmar and Naveed, Sidra and Jugovac, Michael}, editor = {Bridge, Derek and Stuckenschmidt, Heiner}, title = {User Control in Recommender Systems: Overview and Interaction Challenges}, booktitle = {E-Commerce and Web Technologies}, year = {2017}, publisher = {Springer International Publishing}, pages = {21–33}, abstract = {Recommender systems have shown to be valuable tools that help users find items of interest in situations of information overload. These systems usually predict the relevance of each item for the individual user based on their past preferences and their observed behavior. If the system’s assumption about the users’ preferences are however incorrect or outdated, mechanisms should be provided that put the user into control of the recommendations, e.g., by letting them specify their preferences explicitly or by allowing them to give feedback on the recommendations. In this paper we review and classify the different approaches from the research literature of putting the users into active control of what is recommended. We highlight the challenges related to the design of the corresponding user interaction mechanisms and finally present the results of a survey-based study in which we gathered user feedback on the implemented user control features on Amazon.} } @inproceedings{ubo_mods_00108262, author = {Schäfer, Hanna and Hors-Fraile, Santiago and Karumur, Pavan Raghav and Valdez, Calero André and Said, Alan and Torkamaan, Helma and Ulmer, Tom and Trattner, Christoph}, title = {Towards Health (Aware) Recommender Systems}, booktitle = {Proceedings of the 2017 International Conference on Digital Health}, year = {2017}, publisher = {ACM}, address = {New York}, pages = {157–161}, keywords = {Patient modeling}, isbn = {978-1-4503-5249-9}, doi = {10.1145/3079452.3079499} } @inproceedings{ubo_mods_00106644, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, editor = {}, title = {Improving the Shopping Experience with an Augmented Reality-Enhanced Shelf}, booktitle = {Mensch und Computer 2017 - Workshopband}, year = {2017}, pages = {629–632}, keywords = {augmented reality; enhanced retailing; human-computer interaction}, doi = {10.18420/muc2017-demo-0351}, language = {en} } @inproceedings{ubo_mods_00094630, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Boratto, Ludovico and Carta, Salvatore and Fenu, Gianni}, chapter = {}, title = {Towards a Design Space for Personalizing the Presentation of Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1945}, pages = {10–17}, keywords = {Interactive control}, url = {http://ceur-ws.org/Vol-1945/paper_3.pdf}, abstract = {Although personalization plays a major role in the development of recommender systems, the presentation of recommendations and especially the way in which it can be adapted to suit the user’s needs has received relatively little attention from the research community. We introduce a design space for personalizing the presentation of recommendations and propose several dimensions that should be a part of it. Moreover, we present our initial insights about possible interactive mechanisms as well as potential evaluation criteria. Our goal is to provide a systematic way of designing personalized recommendation content, which should prove benecial for other researchers working on this topic. In the longer term, we are interested to investigate whether such personalized presentation implementations influence the perceived trustworthiness of the recommendations.}, booktitle = {EnCHIReS 2017: Engineering Computer-Human Interaction in Recommender Systems : Proceedings of the Second Workshop on Engineering Computer-Human Interaction in Recommender Systems co-located with the 9th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2017)} } @inproceedings{ubo_mods_00090488, author = {Donkers, Tim and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {Sequential User-based Recurrent Neural Network Recommendations}, year = {2017}, publisher = {ACM}, address = {New York, NY, USA}, pages = {152–160}, keywords = {Sequential Recommendations}, doi = {10.1145/3109859.3109877}, url = {https://dl.acm.org/doi/10.1145/3109859.3109877?cid=87958660357}, abstract = {Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network.}, booktitle = {Proceedings of the 11th ACM Conference on Recommender Systems (RecSys ’17)} } @inproceedings{ubo_mods_00090298, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Neidhardt, Julia and Fesenmaier, Daniel and Kuflik, Tsvi and Wörndl, Wolfgang}, chapter = {}, title = {Co-Staying: a Social Network for Increasing the Trustworthiness of Hotel Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1906}, pages = {35–39}, keywords = {Trustworthiness}, abstract = {Recommender systems attempt to match users’ preferences with items. To achieve this, they typically store and process a large amount of user profiles, item attributes, as well as an ever-increasing volume of user-generated feedback about those items. By mining user-generated data, such as reviews, a complex network consisting of users, items, and item properties can be created. Exploiting this network could allow a recommender system to identify, with greater accuracy, items that users are likely to find attractive based on the attributes mentioned in their past reviews as well as in those left by similar users. At the same time, allowing users to visualize and explore the network could lead to novel ways of interacting with recommender systems and might play a role in increasing the trustworthiness of recommendations. We report on a conceptual model for a multimode network for hotel recommendations and discuss potential interactive mechanisms that might be employed for visualizing it.}, url = {http://ceur-ws.org/Vol-1906/paper6.pdf}, booktitle = {RecTour 2017: 2nd Workshop on Recommenders in Tourism : Proceedings of the 2nd Workshop on Recommenders in Tourism co-located with 11th ACM Conference on Recommender Systems (RecSys 2017) Como, Italy, August 27, 2017} } @inproceedings{ubo_mods_00090297, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Domonkos, Tikk and Pu, Pearl}, chapter = {}, title = {Users’ Choices About Hotel Booking: Cues for Personalizing the Presentation of Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1905}, pages = {44–45}, keywords = {Tourism}, abstract = {Personalization in recommender systems has typically been applied to the underlying algorithms. In contrast, the presentation of individual recommendations—specifically, the various ways in which it can be adapted to suit the user’s needs in a more effective manner—has received relatively little attention by comparison. We present the results of an exploratory survey about users’ choices regarding hotel recommendations and draw preliminary conclusions about whether these choices can influence the presentation of recommendations.}, url = {http://ceur-ws.org/Vol-1905/recsys2017_poster22.pdf}, booktitle = {Poster Proceeding of ACM Recsys 2017: Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems (RecSys 2017) Como, Italy, August 28, 2017} } @inproceedings{ubo_mods_00089204, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Brusilovsky, Peter and de Gemmis, Marco and Felfernig, Alexander and Lops, Pasquale and O’Donovan, John and Tintarev, Nava and Willemsen, C. Martijn}, chapter = {}, title = {User Model Dimensions for Personalizing the Presentation of Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1884}, pages = {20–23}, keywords = {User profile}, abstract = {Personalization in recommender systems has typically been applied to the underlying algorithms and to the predicted result sets. Meanwhile, the presentation of individual recommendations—specifically, the various ways in which it can be adapted to suit the user’s needs in a more effective manner—has received relatively little attention by comparison. A limiting factor for the design of such interactive and personalized presentations is the quality of the user data, such as elicited preferences, that is available to the recommender system. At the same time, many of the existing user models are not optimized sufficiently for this specific type of personalization. We present the results of an exploratory survey about users’ choices regarding the presentation of hotel recommendations. Based on our analysis, we propose several novel dimensions to the conventional user models exploited by recommender systems. We argue that augmenting user profiles with this range of information would facilitate the development of more interactive mechanisms for personalizing the presentation of recommendations. This, in turn, could lead to increased transparency and control over the recommendation process.}, url = {http://ceur-ws.org/Vol-1884/paper4.pdf}, booktitle = {IntRS 2017: Interfaces and Human Decision Making for Recommender Systems : Proceedings of the 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2017)} } @inproceedings{ubo_mods_00089097, author = {Feuerbach, Jan and Loepp, Benedikt and Barbu, Catalin-Mihai and Ziegler, Jürgen}, title = {Enhancing an Interactive Recommendation System with Review-based Information Filtering}, booktitle = {Proceedings of the 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS ’17)}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1884}, pages = {2–9}, keywords = {User Reviews}, abstract = {Integrating interactive faceted filtering with intelligent recommendation techniques has shown to be a promising means for increasing user control in Recommender Systems. In this paper, we extend the concept of blended recommending by automatically extracting meaningful facets from social media by means of Natural Language Processing. Concretely, we allow users to influence the recommendations by selecting facet values and weighting them based on information other users provided in their reviews. We conducted a user study with an interactive recommender implemented in the hotel domain. This evaluation shows that users are consequently able to find items fitting interests that are typically difficult to take into account when only structured content data is available. For instance, the extracted facets representing the opinions of hotel visitors make it possible to effectively search for hotels with comfortable beds or that are located in quiet surroundings without having to read the user reviews.}, url = {http://ceur-ws.org/Vol-1884/paper1.pdf} } @inproceedings{ubo:80746, author = {Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {On User Awareness in Model-Based Collaborative Filtering Systems}, year = {2017}, keywords = {User Experience}, url = {https://iuiaware2017.files.wordpress.com/2016/11/on_user_awareness_in_model-based_collaborative_filtering_systems2.pdf}, abstract = {In this paper, we discuss several aspects that users are typically not fully aware of when using model-based Collaborative Filtering systems. For instance, the methods prevalently used in conventional recommenders infer abstract models that are opaque to users, making it difficult to understand the learned profile, and consequently, why certain items are recommended. Further, users are not able to keep an overview of the item space, and thus the alternatives that in principle could also be suggested. By summarizing our experiences on exploiting latent factor models for increasing control and transparency, we show that the respective techniques may also contribute to make users more aware of their preferences’ representation, the rationale behind the results, and further items of potential interest.}, booktitle = {Proceedings of the 1st Workshop on Awareness Interfaces and Interactions (AWARE ’17)} } @inproceedings{ubo:80745, author = {Kunkel, Johannes and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering}, year = {2017}, publisher = {ACM}, address = {New York, NY, USA}, pages = {3–15}, keywords = {3D Visualizations}, doi = {10.1145/3025171.3025189}, url = {https://dl.acm.org/doi/10.1145/3025171.3025189?cid=87958660357}, abstract = {While conventional Recommender Systems perform well in automatically generating personalized suggestions, it is often difficult for users to understand why certain items are recommended and which parts of the item space are covered by the recommendations. Also, the available means to influence the process of generating results are usually very limited. To alleviate these problems, we suggest a 3D map-based visualization of the entire item space in which we position and present sample items along with recommendations. The map is produced by mapping latent factors obtained from Collaborative Filtering data onto a 2D surface through Multidimensional Scaling. Then, areas that contain items relevant with respect to the current user’s preferences are shown as elevations on the map, areas of low interest as valleys. In addition to the presentation of his or her preferences, the user may interactively manipulate the underlying profile by raising or lowering parts of the landscape, also at cold-start. Each change may lead to an immediate update of the recommendations. Using a demonstrator, we conducted a user study that, among others, yielded promising results regarding the usefulness of our approach.}, booktitle = {Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI ’17)} } @inproceedings{ubo:74508, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, editor = {Yuizono, Takaya and Ogata, Hiroaki and Hoppe, Ulrich Heinz and Vassileva, Julita}, chapter = {}, title = {Hootle+: A Group Recommender System Supporting Preference Negotiation}, series = {Lecture Notes in Computer Science}, year = {2016}, publisher = {Springer}, address = {Cham}, volume = {9848}, pages = {151–166}, keywords = {Decision-Making}, isbn = {978-3-319-44799-5}, doi = {10.1007/978-3-319-44799-5_12}, url = {https://link.springer.com/chapter/10.1007/978-3-319-44799-5_12}, abstract = {This paper presents an approach to group recommender systems that focuses its attention on the group’s social interaction during the formulation, discussion and negotiation of the features the item to be jointly selected should possess. The system supports a collaborative preference elicitation and negotiation process where desired item features can be defined individually, but group consensus is needed for them to become active in the item filtering process. Users can provide feedback on other members’ preferences and change their significance, bringing up new recommendations each time individual settings are modified. The last stage in the decision process is also supported, when users collectively select the final item from the recommendation set. We developed the prototype hotel recommender Hootle+ and evaluated it in a user study involving groups of different size. The results indicate a good overall satisfaction, which increases with group size. However, the success ratio for bigger groups is lower than for small groups, raising questions for follow-up research. }, booktitle = {Collaboration and Technology: 22nd International Conference, CRIWG 2016, Kanazawa, Japan, September 14-16, 2016, Proceedings} } @inproceedings{ubo:73248, author = {Loepp, Benedikt and Barbu, Catalin-Mihai and Ziegler, Jürgen}, chapter = {}, title = {Interactive Recommending: Framework, State of Research and Future Challenges}, year = {2016}, pages = {3–13}, keywords = {Survey}, abstract = {In this paper, we present a framework describing the various aspects of recommender systems that can serve for empowering users by giving them more interactive control and transparency in the recommendation process. While conventional recommenders mostly operate like black boxes that cannot be influenced by the user, we identify four aspects properly connected with the recommendation algorithm—namely input data, user model, external con-text model and presentation—as essential points in which a system may be enhanced by additional interaction possibilities. In light of this framework, we take a closer look at prior and present solutions to integrate recommender systems with more interactivity and describe future research challenges. Regarding these challenges, we especially focus on experiences gained in our own work and outline future research we have planned in the area of interactive recommending.}, url = {http://ceur-ws.org/Vol-1705/02-paper.pdf}, booktitle = {Proceedings of the 1st Workshop on Engineering Computer-Human Interaction in Recommender Systems (EnCHIReS ’16)} } @inproceedings{ubo:72486, author = {Barbu, Catalin-Mihai}, chapter = {}, title = {Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources}, year = {2016}, address = {New York, NY, USA}, publisher = {ACM}, pages = {447–450}, keywords = {recommender systems}, abstract = {Current recommender systems mostly do not take into account as well as they might the wealth of information available in social media, thus preventing the user from obtaining a broad and reliable overview of different opinions and ratings on a product. Furthermore, there is a lack of user control over the recommendation process–which is mostly fully automated and does not allow the user to influence the sources and mechanisms by which recommendations are produced–as well as over the presentation of recommended items. Consequently, recommendations are often not transparent to the user, are considered to be less trustworthy, or do not meet the user’s situational needs. This work will investigate the theoretical foundations for user-controllable, interactive methods of recommending, will develop techniques that exploit social media data in conjunction with other sources, and will validate the research empirically in the area of e-commerce product recommendations. The methods developed are intended to be applicable in a wide range of recommending and decision support scenarios.}, isbn = {978-1-4503-4035-9}, doi = {10.1145/2959100.2959104}, url = {https://dl.acm.org/citation.cfm?id=2959104}, booktitle = {Proceedings of the 10th ACM Conference on Recommender Systems} } @inproceedings{ubo:72538, author = {Donkers, Tim and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {Towards Understanding Latent Factors and User Profiles by Enhancing Matrix Factorization with Tags}, year = {2016}, keywords = {Explanations}, url = {http://ceur-ws.org/Vol-1688/paper-20.pdf}, abstract = {With the interactive recommending approach we have recently proposed, users are given more control over model-based Collaborative Filtering while the results are perceived as more transparent. Integrating the latent factors derived by Matrix Factorization with tags users provided for the items has, however, even more advantages. In this paper, we show how general understanding of the abstract factor space, and of user and item positions inside it, can benefit from the semantics introduced by considering additional information. Moreover, our approach allows us to explain the user’s (former latent) preference profile by means of tags.}, booktitle = {Poster Proceedings of the 10th ACM Conference on Recommender Systems (RecSys ’16)} } @inproceedings{ubo:69909, author = {Donkers, Tim and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {Tag-Enhanced Collaborative Filtering for Increasing Transparency and Interactive Control}, year = {2016}, publisher = {ACM}, address = {New York, NY, USA}, pages = {169–173}, keywords = {User Experience}, abstract = {To increase transparency and interactive control in Recommender Systems, we extended the Matrix Factorization technique widely used in Collaborative Filtering by learning an integrated model of user-generated tags and latent factors derived from user ratings. Our approach enables users to manipulate their preference profile expressed implicitly in the (intransparent) factor space through explicitly presented tags. Furthermore, it seems helpful in cold-start situations since user preferences can be elicited via meaningful tags instead of ratings. We evaluate this approach and present a user study that to our knowledge is the most extensive empirical study of tag-enhanced recommending to date. Among other findings, we obtained promising results in terms of recommendation quality and perceived transparency, as well as regarding user experience, which we analyzed by Structural Equation Modeling.}, doi = {10.1145/2930238.2930287}, url = {https://dl.acm.org/doi/10.1145/2930238.2930287?cid=87958660357}, booktitle = {Proceedings of the 24th Conference on User Modeling Adaptation and Personalization (UMAP ’16)} } @inproceedings{ubo:67231, author = {Herrmanny, Katja and Ziegler, Jürgen and Dogangün, Aysegül and PERSUASIVE 2016}, editor = {Meschtscherjakov, Alexander and De Ruyter, Boris and Fuchsberger, Verena and Murer, Martin and Tscheligi, Manfred}, chapter = {}, title = {Supporting users in setting effective goals in activity tracking}, series = {Lecture notes in computer science}, year = {2016}, publisher = {Springer International Publishing}, address = {Cham}, volume = {9638}, pages = {15–26}, isbn = {978-3-319-31509-6}, doi = {10.1007/978-3-319-31510-2_2}, booktitle = {Persuasive Technology: 11th International Conference ; PERSUASIVE 2016 ; Salzburg, Austria, April 5-7, 2016 ; Proceedings} } @inproceedings{ubo:57963, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, editor = {Abascal, Julio and Barbosa, Simone and Fetter, Mirko and Gross, Tom and Palanque, Philippe and Winckler, Marco}, chapter = {}, title = {Preference Elicitation and Negotiation in a Group Recommender System}, series = {Lecture Notes in Computer Science}, year = {2015}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-22667-5}, url = {http://dx.doi.org/10.1007/978-3-319-22668-2_2}, abstract = {We present a novel approach to group recommender systems that better takes into account the social interaction in a group when formulating, discussing and negotiating the features of the item to be jointly selected. Our approach provides discussion support in a collaborative preference elicitation and negotiation process. Individual preferences are continuously aggregated and immediate feedback of the resulting recommendations is provided. We also support the last stage in the decision process when users collectively select the final item from the recommendation set. The prototype hotel recommender Hootle is developed following these concepts and tested in a user study. The results indicate a higher overall satisfaction with the system as well as a higher perceived recommendation quality when compared against a system version where no negotiation was possible. However, they also indicate that the negotiation-based approach may be more suitable for smaller groups, an aspect that will require further research.}, booktitle = {Human-Computer Interaction – INTERACT 2015: 15th IFIP TC 13 International Conference, Bamberg, Germany, September 14-18, 2015, Proceedings, Part II} } @inproceedings{ubo:57156, author = {Donkers, Tim and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {Merging Latent Factors and Tags to Increase Interactive Control of Recommendations}, year = {2015}, abstract = {We describe a novel approach that integrates user-generated tags with standard Matrix Factorization to allow users to interactively control recommendations. The tag information is incorporated during the learning phase and relates to the automatically derived latent factors. Thus, the system can change an item’s score whenever the user adjusts a tag’s weight. We implemented a prototype and performed a user study showing that this seems to be a promising way for users to interactively manipulate the set of items recommended based on their user profile or in cold-start situations.}, url = {http://ceur-ws.org/Vol-1441/recsys2015_poster12.pdf}, booktitle = {Poster Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15)} } @inproceedings{ubo:57124, author = {Kunkel, Johannes and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {3D-Visualisierung zur Eingabe von Präferenzen in Empfehlungssystemen}, year = {2015}, pages = {123–132}, publisher = {De Gruyter Oldenbourg}, address = {Berlin}, abstract = {In diesem Beitrag stellen wir ein interaktives Empfehlungssystem vor, bei dem Nutzer ihre Präferenzen in einer dreidimensionalen Visualisierung des Produktraums eingeben können. Die Darstellung in Form einer Landschaft spiegelt dabei das Profil des aktuellen Nutzers wider, und ermöglicht diesem sowohl in Kaltstartsituationen als auch bei der späteren Anpassung eines existierenden Profils interaktiv seine Präferenzen anzugeben. Die Methode basiert auf den von allen Nutzern abgegebenen Bewertungen und benötigt kein inhaltliches Wissen über die Produkte. Die durchgeführte Nutzerstudie zeigt, dass die Visualisierung nachvollziehbar und hilfreich erscheint. Bezüglich der Eingabe von Präferenzen durch Modellierung der Landschaft ergaben sich ebenfalls vielversprechende Ergebnisse, u. a. auch im Hinblick auf User Experience und Empfehlungsqualität.}, doi = {10.1515/9783110443929-014}, url = {http://dx.doi.org/10.1515/9783110443929-014}, booktitle = {Mensch und Computer 2015 – Tagungsband} } @article{ubo:54267, author = {Loepp, Benedikt and Herrmanny, Katja and Ziegler, Jürgen}, title = {Merging Interactive Information Filtering and Recommender Algorithms: Model and Concept Demonstrator}, journal = {i-com}, year = {2015}, volume = {14}, number = {1}, pages = {5–17}, issn = {2196-6826}, doi = {10.1515/icom-2015-0006}, url = {http://dx.doi.org/10.1515/icom-2015-0006}, abstract = {To increase controllability and transparency in recommender systems, recent research has been putting more focus on integrating interactive techniques with recommender algorithms. In this paper, we propose a model of interactive recommending that structures the different interactions users can have with recommender systems. Furthermore, as a novel approach to interactive recommending, we describe a technique that combines faceted information filtering with different algorithmic recommender techniques. We refer to this approach as blended recommending. We also present an interactive movie recommender based on this approach and report on its user-centered design process, in particular an evaluation study in which we compared our system with a standard faceted filtering system. The results indicate a higher level of perceived user control, more detailed preference settings, and better suitability when the search goal is vague.} } @inproceedings{ubo:53366, author = {Loepp, Benedikt and Herrmanny, Katja and Ziegler, Jürgen}, chapter = {}, title = {Blended Recommending: Integrating Interactive Information Filtering and Algorithmic Recommender Techniques}, year = {2015}, publisher = {ACM}, address = {New York, NY, USA}, abstract = {We present a novel approach that integrates algorithmic recommender techniques with interactive faceted filtering methods. We refer to this approach as blended recommending. It allows users to interact with a set of filter facets representing criteria that can serve as input for different recommendation methods including both collaborative and content-based filtering. Users can select filter criteria from these facets and weight them to express their preferences and to exert control over the hybrid recommendation process. In contrast to hard Boolean filtering, the method aggregates the weighted criteria and calculates a ranked list of recommendations that is visualized and immediately updated when users change the filter settings. Based on this approach, we implemented an interactive movie recommender, MyMovieMixer. In a user study, we compared the system with a conventional faceted filtering system that served as a baseline to obtain insights into user interaction behavior and to assess recommendation quality for our system. The results indicate, among other findings, a higher level of perceived user control, more detailed preference settings, and better suitability when the search goal is vague.}, doi = {10.1145/2702123.2702496}, pages = {975–984}, url = {https://dl.acm.org/doi/10.1145/2702123.2702496?cid=87958660357}, booktitle = {Proceedings of the 33rd International Conference on Human Factors in Computing Systems (CHI ’15)} } @article{ubo_mods_00082812, author = {Hussein, Tim and Linder, Timm and Gaulke, Werner and Ziegler, Jürgen}, title = {Hybreed: a software framework for developing context-aware hybrid recommender systems}, journal = {User modeling and user adapted interaction}, year = {2014}, publisher = {Proquest}, address = {[S.l.]}, volume = {24}, number = {1}, pages = {121–174}, keywords = {Recommender systems}, issn = {1573-1391}, doi = {10.1007/s11257-012-9134-z} } @inproceedings{ubo:48983, author = {Herrmanny, Katja and Schering, Sandra and Berger, Ralf and Loepp, Benedikt and Günter, Timo and Hussein, Tim and Ziegler, Jürgen}, chapter = {}, title = {MyMovieMixer: Ein hybrider Recommender mit visuellem Bedienkonzept}, year = {2014}, publisher = {De Gruyter Oldenbourg}, address = {Berlin}, pages = {45–54}, doi = {10.1524/9783110344486.45}, url = {http://dx.doi.org/10.1524/9783110344486.45}, abstract = {In diesem Beitrag stellen wir ein neuartiges, auf direkter Manipulation beruhendes Bedienkonzept für komplexe hybride Empfehlungssysteme anhand des von uns entwickelten Film-Recommenders MyMovieMixer vor. Der Ansatz ermöglicht es den Nutzern, ein hybrides Recommender-System mit einem komplexen Zusammenwirken verschiedener Filtermethoden durch interaktive und visuelle Methoden intuitiv zu steuern. Gleichzeitig wird die Transparenz der Empfehlungsgenerierung deutlich erhöht. Die Ergebnisse einer empirischen Evaluation des Systems zeigen, dass der Ansatz in Bezug auf Usability, User Experience, Intuitivität, Transparenz, wahrgenommene Empfehlungsqualität und somit letztlich im Hinblick auf die Nutzerzufriedenheit vielversprechend ist. }, booktitle = {Mensch und Computer 2014 – Tagungsband} } @inproceedings{ubo:46601, author = {Loepp, Benedikt and Hussein, Tim and Ziegler, Jürgen}, chapter = {}, title = {Choice-based Preference Elicitation for Collaborative Filtering Recommender Systems}, year = {2014}, pages = {3085–3094}, publisher = {ACM}, address = {New York, NY, USA}, isbn = {978-1-4503-2473-1}, doi = {10.1145/2556288.2557069}, abstract = {We present an approach to interactive recommending that combines the advantages of algorithmic techniques with the benefits of user-controlled, interactive exploration in a novel manner. The method extracts latent factors from a matrix of user rating data as commonly used in Collaborative Filtering, and generates dialogs in which the user iteratively chooses between two sets of sample items. Samples are chosen by the system for low and high values of each latent factor considered. The method positions the user in the latent factor space with few interaction steps, and finally selects items near the user position as recommendations. In a user study, we compare the system with three alternative approaches including manual search and automatic recommending. The results show significant advantages of our approach over the three competing alternatives in 15 out of 24 possible parameter comparisons, in particular with respect to item fit, interaction effort and user control. The findings corroborate our assumption that the proposed method achieves a good trade-off between automated and interactive functions in recommender systems.}, url = {https://dl.acm.org/doi/10.1145/2556288.2557069?cid=87958660357}, booktitle = {Proceedings of the 32nd International Conference on Human Factors in Computing Systems (CHI ’14)} } @article{ubo:42845, author = {Hussein, Tim and Linder, Timm and Gaulke, Werner and Ziegler, Jürgen}, title = {Hybreed: A Software Framework for Developing Context-Aware Hybrid Recommender Systems}, journal = {User modeling and user adapted interaction}, abstract = {This article introduces Hybreed, a software framework for building complex context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a concept for processing context that we call Dynamic Contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, we describe Hybreed RecViews, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups. The framework provides a range of recommendation algorithms and strategies for producing group recommendations as well as templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner. To evaluate the framework and its conceptual foundation, we verified its capabilities in three different use cases. The evaluation also comprises a comparative assessment of Hybreed’s functional features, a comparison to existing frameworks, and a user study assessing its usability for developers. The results of this study indicate that Hybreed is intuitive to use and extend by developers.}, year = {2014}, volume = {24}, number = {1}, pages = {121–174}, issn = {1573-1391}, doi = {10.1007/s11257-012-9134-z} } @incollection{ubo_mods_00080806, author = {Taghavi, Mona and Bakhtiyari, Kaveh and Scavino, Edgar}, title = {Agent-based Computational Investing Recommender System}, booktitle = {Proceedings of the 7th ACM Conference on Recommender Systems}, series = {RecSys ’13}, year = {2013}, isbn = {978-1-4503-2409-0}, location = {Hong Kong, China}, pages = {455–458}, numpages = {4}, url = {http://doi.acm.org/10.1145/2507157.2508072}, doi = {10.1145/2507157.2508072}, acmid = {2508072}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {computational investing, hybrid filtering, multi-agent system, recommender system, stock market} } @book{ubo:42846, author = {Hussein, Tim}, title = {A conceptual model and a software framework for developing context aware hybrid recommender systems}, year = {2013}, publisher = {Hut}, address = {München}, abstract = {This thesis introduces Hybreed, a software framework for building context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a conceptual model called Dynamic Contextualization, which is also introduced in this thesis. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user-system interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, Hybreed RecViews is introduced, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups.}, isbn = {978-3-8439-1188-7}, school = {University of Duisburg-Essen} } @inproceedings{ubo:42827, author = {Kaindl, Hermann and Wach, Elmar and Okoli, Ada and Popp, Roman and Hoch, Ralph and Gaulke, Werner and Hussein, Tim}, chapter = {}, title = {Semi-automatic generation of recommendation processes and their GUIs}, year = {2013}, publisher = {ACM}, abstract = {Creating and optimizing content- and dialogue-based recommendation processes and their GUIs (graphical user interfaces) manually is expensive and slow. Changes in the environment may also be found too late or even be overlooked by humans. We show how to generate such processes and their GUIs semi-automatically by using knowledge derived from unstructured data such as customer feedback on products on the Web. Our approach covers the whole lifecycle from knowledge discovery through text mining techniques to the use of this knowledge for semi-automatic generation of recommendation processes and their user interfaces as well as their comparison in real-world use within the e-commerce domain through A/B-variant tests. These tests indicate that our approach can lead to better results as well as less manual effort.}, isbn = {978-1-4503-1965-2}, url = {http://dx.doi.org/10.1145/2449396.2449410}, booktitle = {Proceedings of the 18th International Conference on Intelligent User Interfaces (IUI ’13)} } @inproceedings{ubo:42828, author = {Loepp, Benedikt and Hussein, Tim and Ziegler, Jürgen}, chapter = {}, title = {Interaktive Empfehlungsgenerierung mit Hilfe latenter Produktfaktoren}, year = {2013}, publisher = {Oldenbourg}, address = {München}, abstract = {In diesem Beitrag beschreiben wir ein Verfahren zur Generierung interaktiver Empfehlungsdialoge auf Basis latenter Produktfaktoren. Der Ansatz verbindet auf neuartige Weise Methoden zur automatischen Generierung von Empfehlungen mit interaktiven, explorativen Methoden der Produktsuche. Das vorgestellte Verfahren nutzt verborgene Muster in Produktbewertungen (latente Faktoren) und erzeugt auf dieser Basis visuelle Dialoge, die den Nutzer schrittweise und intuitiv durch einen Explorationsprozess führen. In einer Nutzerstudie konnten wir zeigen, dass ein derartiger interaktiver Empfehlungsprozess hinsichtlich des Aufwandes und der Zufriedenheit mit den erzielten Resultaten eine deutliche Verbesserung gegenüber rein manuellen oder rein automatischen Verfahren bieten kann.}, pages = {17–26}, doi = {10.1524/9783486781229.17}, url = {http://dx.doi.org/10.1524/9783486781229.17}, booktitle = {Mensch & Computer 2013 – Tagungsband} } @inproceedings{ubo:42844, author = {Hussein, Tim and Linder, Timm and Ziegler, Jürgen}, editor = {Hussein, Tim and Paulheim, Heiko and Lukosch, Stephan and Ziegler, Jürgen and Calvary, Gaelle}, chapter = {}, title = {A Context-Aware Shopping Portal Based on Semantic Models}, series = {Human-Computer Interaction Series}, year = {2013}, publisher = {Springer}, address = {London}, abstract = {This chapter illustrates how semantic models can be used as a backend data source for both exploration and adaptation purposes. For a fictitious shopping portal, we implemented a faceted navigation approach that provides means for exploring the portal’s content manually. In addition to that, we implemented an adaptation mechanism based on spreading activation that also exploits the semantic structure of the underlying data.}, isbn = {978-1-4471-5301-6}, url = {http://dx.doi.org/10.1007/978-1-4471-5301-6_8}, booktitle = {Semantic Models for Adaptive Interactive Systems} } @inproceedings{Munter:2012, author = {Münter, Daniel and Hussein, Tim and Gaulke, Werner and Ziegler, Jürgen}, title = {Service-Based Recommendations for Context-Aware Navigation Support}, booktitle = {Zukünftige Entwicklungen in der Mobilität: Betriebswirtschaftliche und Technische Aspekte - Tagungsband des 3. Wissenschaftsforum Mobilität}, edition = {1. Auflage}, editor = {Proff, Heike and Schönharting, Jörg and Schramm, Dieter and Ziegler, Jürgen}, isbn = {978-3-8349-3232-7}, publisher = {Gabler Verlag}, address = {Wiesbaden}, url = {http://www.gabler.de/Buch/978-3-8349-3232-7/}, year = {2012}, abstract = {In this paper, we introduce a novel concept for service integration into navigation systems. Our approach incorporates contextual information (such as the current location or route) as well as information retrieved from web-based services such as hotel ratings or gas prices. We present a generic framework that can be used to design navigation systems based on such information and describe a prototypical implementation.} } @article{ubo:28968, author = {Hussein, Tim and Ziegler, Jürgen}, title = {Situationsgerechtes Recommending – Kontextadaptive, hybride Empfehlungsgenerierung}, journal = {Informatik Spektrum}, year = {2011}, volume = {34}, number = {2}, pages = {143–152}, abstract = {Dieser Artikel untersucht die unterschiedlichen Paradigmen, die kontextadaptiven Empfehlungssystemen zugrunde liegen und schlägt einen neuen perspektivenorientierten Ansatz vor. Kontext kann demnach nicht nur als vorab festgelegte Menge vorliegender Gegebenheiten (repräsentationaler Ansatz) oder in Wechselwirkung zur aktuellen Tätigkeit (interaktionaler Ansatz) gesehen werden, sondern als eine sich dynamisch ändernde Perspektive, unter der eine vorliegende Situation zu beurteilen ist. Mit Context Views führen wir eine Methode ein, mit der auf diese Weise kontextsensitive Empfehlungen generiert werden können. Weiterhin wird ein Framework vorgestellt, das in flexibler Weise kontextabhängig unterschiedliche Strategien zur Empfehlungsgenerierung in einem hybriden Ansatz integrieren kann.}, url = {http://www.springerlink.com/content/g63v543125526334/} } @inproceedings{ubo:27688, author = {Hussein, Tim and Gaulke, Werner and Hartmann, Anabell and Ziegler, Jürgen}, editor = {Ziegler, Jürgen and Schmidt, Albrecht}, chapter = {}, title = {Wahrnehmung und Akzeptanz von systemgenerierten Produktempfehlungen}, year = {2010}, edition = {1}, publisher = {Oldenbourg}, address = {München}, abstract = {Seit mehr als einem Jahrzehnt werden Empfehlungssysteme (Recommender Systems) in Webshops, Nachrichtenportalen und anderen Bereichen eingesetzt, um die Nutzer zielgerichtet zu potenziell interessanten Produkten und Inhalten zu führen. Während seit vielen Jahren intensiv an der Verbesserung der Algorithmen zur Empfehlungsgenerierung geforscht wird, ist jedoch wenig darüber bekannt, welche Faktoren – neben der Qualität der Empfehlungen an sich – für die Wahrnehmung und Akzeptanz systemgenerierter Empfehlungen verantwortlich sind. Dieser Beitrag präsentiert die Ergebnisse einer Studie, in der der Einfluss von Faktoren wie Kenntnis der durchsuchten Produktdomäne, Preisniveau der Produkte und Zeitdruck untersucht werden. Die Ergebnisse der Studie zeigen, dass Kenntnis der Produktdomäne sowie der Preisbereich der Produkte Einfluss auf die oben angesprochenen Größen hatten. Zeitdruck hingegen erwies sich nicht als relevanter Faktor.}, isbn = {978-3-486-70408-2}, booktitle = {Mensch & Computer 2010} } @inproceedings{ubo:26372, author = {Münter, Daniel and Hussein, Tim and Gaulke, Werner}, editor = {Schroeder, Ulrik}, chapter = {}, title = {Kontextabhängige Empfehlung von Services zur intelligenten Navigationsunterstützung}, year = {2010}, edition = {1. Auflage}, publisher = {Logos Verlag}, address = {Berlin}, abstract = {In diesem Beitrag stellen wir ein neuartiges Konzept zur Integration von Diensten zur intelligenten Navigationsunterstützung vor. Unser Ansatz berücksichtigt Kontextinformationen (wie beispielsweise der aktuelle Ort oder die gefahrene Route) ebenso wie Daten, welche über web-basierte Dienstleistungsangebote (z.B. Hotelbewertungen oder Benzinpreise) ermittelt werden können. Wir präsentieren ein generisches Framework, welches zur Entwicklung von Navigationssystemen verwendet werden kann, die solche Dienste einbinden.}, isbn = {978-3-8325-2578-1}, url = {http://www.logos-verlag.de/cgi-bin/engbuchmid?isbn=2578&lng=deu&id=0}, booktitle = {Interaktive Kulturen - Proceedings der Workshops der Mensch & Computer 2010 - 10. fachübergreifende Konferenz für interaktive und kooperative Medien, DeLFI 2010 - Die 8. E-Learning Fachtagung Informatik der Gesellschaft für Informatik e.V. und der Entertainment Interfaces 2010} } @article{ubo:27690, author = {Hussein, Tim and Gaulke, Werner}, title = {Hybride, kontext-sensitive Generierung von Produktempfehlungen}, journal = {i-com – Zeitschrift für interaktive und kooperative Medien}, year = {2010}, volume = {9}, number = {2}, pages = {16–23}, abstract = {In diesem Beitrag stellen wir mit Hybreed RecFlows ein modulares Framework zur Generierung von (Produkt) Empfehlungen vor. RecFlows (Kurzform für Recommendation Workflows) stellt eine Reihe etablierter Algorithmen aus dem Bereich Recommender Systems zur Verfügung sowie einen Workflow-Mechanismus, um aus diesen Algorithmen flexibel hybride Recommender zu erstellen. Darüber hinaus werden unterschiedliche Sensoren bereitgestellt, um Informationen aus verschiedenen Quellen in den Empfehlungsprozess mit einfließen zu lassen. Insbesondere werden Sensoren zur Kontext-Erfassung (z. B. der aktuelle Ort des Nutzers anhand seiner IP-Adresse) implementiert. So ist es möglich, mit Hilfe von RecFlows hybride, kontextsensitive Empfehlungen zu generieren. Read More: http://www.oldenbourg-link.com/doi/abs/10.1524/icom.2010.0018}, url = {http://www.oldenbourg-link.com/doi/abs/10.1524/icom.2010.0018} } @inproceedings{ubo:24883, author = {Hussein, Tim and Neuhaus, Sebastian}, chapter = {}, title = {Explanation of spreading activation based recommendations}, year = {2010}, address = {Hong Kong, China}, url = {http://duepublico.uni-duisburg-essen.de/servlets/DocumentServlet?id=21925}, abstract = {In this paper, we introduce an approach for explaining rec- ommendations in environments that are based on semantic models. Using a constrained Spreading Activation (CSA) technique for recommendation generation, we store and exploit the activation paths leading to recommendations. These paths later can be used to generate both verbal explanations and relevance feedback forms.}, booktitle = {Semantic Models for Adaptive Interactive Systems (SEMAIS), 1st Workshop in conjunction with the International Conference on Intelligent User Interfaces (IUI) 2010} } @inproceedings{ubo:24768, author = {Hussein, Tim}, editor = {Baloian, Nelson and Luther, Wolfram and Söffker, Dirk and Urano, Yoshiyori}, chapter = {}, title = {Context-aware Recommendations}, year = {2010}, publisher = {Logos}, address = {Berlin}, abstract = {This article illustrates the vivid research field of hybrid and context-aware recommender systems. Moreover, two own approaches to deal with context-awareness in recommender systems, are described in detail.}, isbn = {978-3-8325-2361-9}, url = {http://duepublico.uni-duisburg-essen.de/servlets/DocumentServlet?id=21924}, booktitle = {Interfaces and Interaction Design for Learning and Simulation Environments} } @inproceedings{ubo:27689, author = {Hussein, Tim and Linder, Timm and Gaulke, Werner and Ziegler, Jürgen}, editor = {Kolfschoten, Gwendolyn and Herrmann, Thomas and Lukosch, Stephan}, chapter = {}, title = {A framework and an architecture for context-aware group recommendations}, year = {2010}, publisher = {Springer}, address = {Berlin}, abstract = {In this paper, we propose a generic framework to generate context-aware recommendations for both single users as well as groups. We present the the concept of context views and an corresponding architecture implementing the framework as well as exemplary recommendation workflows for group recommendations.}, isbn = {978-3-642-15714-1}, url = {http://www.springerlink.com/content/et680874862602w6/}, booktitle = {Collaboration and Technology: 16th International Conference, CRIWG 2010, Maastricht, The Netherlands, September 20-23, 2010. Proceedings} } @inproceedings{ubo:22981, author = {Hussein, Tim and Linder, Timm and Gaulke, Werner and Ziegler, Jürgen}, editor = {Bergmann, Lawrence}, chapter = {}, title = {Context-aware Recommendations on Rails}, year = {2009}, address = {New York, NY, USA}, url = {http://duepublico.uni-duisburg-essen.de/servlets/DocumentServlet?id=20980}, booktitle = {Proceedings of the 2009 Workshop on Context-aware Recommender Systems (CARS 2009)} } @inproceedings{ubo:14866, author = {Hussein, Tim and Ziegler, Jürgen}, chapter = {}, title = {Adapting web sites by spreading activation in ontologies}, year = {2008}, address = {Gran Canaria}, booktitle = {ReColl ’08: Int. Workshop on Recommendation and Collaboration (in conjunction with IUI 2008)} } @inproceedings{ubo:16723, author = {Hussein, Tim and Westheide, Daniel and Ziegler, Jürgen}, editor = {Hinneburg, Alexander}, chapter = {}, title = {Context-adaption based on ontologies and spreading activation}, year = {2007}, address = {Martin-Luther-Universität Halle-Wittenberg}, abstract = {Ontologies and spreading activation are known terms within the scope of information retrieval. In this paper we introduce SPREADR, an integrated adaptation mechanism for web applications that uses ontologies for representing the application domain as well as context information like location, user history and local time. Those context factors can be modeled in an ontology and be linked to certain domain nodes. In each session a Spreading Activation Network is build based on those ontologies and recognized context factors or user actions can trigger an activation flow through this network. A node’s resulting activation value then represents its importance according to the current circumstances. While identically in structure, the Spreading Activation Networks are personalized by automatically modifying link weights and activation levels of nodes. As a result the system learns about the user preferences and can adjust its adaptation mechanism for future runs through implicit feedback.}, isbn = {978-3-86010-907-6}, booktitle = {LWA 2007: Lernen – Wissen – Adaption} } @article{ubo:18136, author = {Ziegler, Jürgen and Kaltz, Wolfgang J. and Lohmann, Steffen and Hussein, Tim}, title = {Maßarbeit statt Konfektion - Kontextadaption liefert passgenaue Informationen}, journal = {Forum Forschung}, year = {2006}, pages = {42–47} }