@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.} } @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_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_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_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_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} } @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.} } @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_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_00139552, author = {Kleemann, Timm and Ziegler, Jürgen}, title = {Integration of Dialog-based Product Advisors into Filter Systems}, booktitle = {Proceedings of the Conference on Mensch und Computer}, series = {ACM International Conference Proceeding Series}, year = {2019}, publisher = {ACM Press}, address = {New York}, pages = {67–77}, keywords = {Dialogbasierte Produktberater, Filtersysteme}, isbn = {978-1-4503-7198-8}, doi = {10.1145/3340764.3340786}, abstract = { Different techniques such as search functions or recommendation components are used today to support the often complex product search on the Internet. Faceted filter systems that successively limit the result set according to the set filter settings have proven to be quite successful. However, this method requires clear objectives and domain knowledge on the part of the users. As an alternative, conversational product advisors who select suitable products on the basis of a sequence of questions have gained more importance in recent times, whereby the questions are based more on the tasks and application scenarios of the users than on the technical properties of the products. However, there is currently a lack of approaches that integrate filter systems and conversational advisors in a meaningful and closely coupled way. In this paper an integrated approach is presented, where users can switch between filter systems and advisory dialogues, whereby selection actions in one component have a consistent and transparent effect on the other component and can be further adjusted there. The aim is to better support users with different levels of knowledge of the product type concerned. We describe the requirements for such integrated systems resulting from our approach and report on a user study in which the user behavior and the subjective evaluation were examined in a prototypical implementation.} } @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{ubo_mods_00117943, author = {Kizina, Anna and Kunkel, Johannes and Ziegler, Jürgen}, title = {Ein kollaboratives Task-Management-System mit spielerischen Elementen}, booktitle = {Mensch und Computer 2018: Workshopband}, year = {2018}, publisher = {Gesellschaft für Informatik e.V.}, address = {Bonn}, keywords = {Kollaboration}, issn = {2510-2672}, doi = {10.18420/muc2018-ws03-0477} } @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_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:26020, author = {Lohmann, Steffen and Tomanek, Katrin and Ziegler, Jürgen and Hahn, Udo}, editor = {Ohlsson, Stellan and Catrambone, Richard}, chapter = {}, title = {Getting at the Cognitive Complexity of Linguistic Metadata Annotation: A Pilot Study Using Eye-Tracking}, year = {2010}, publisher = {Cognitive Science Society}, address = {Austin, TX}, abstract = {We report on an experiment where the decision behavior of annotators issuing linguistic metadata is observed with an eye-tracking device. As experimental conditions we consider the role of textual context and linguistic complexity classes. Still preliminary in nature, our data suggests that semantic complexity is much harder to deal with than syntactic one, and that full-scale textual context is negligible for annotation, with the exception of semantic high-complexity cases. We claim that such observational data might lay the foundation for empirically grounded annotation cost models and the design of cognitively adequate annotation user interfaces.}, url = {http://palm.mindmodeling.org/cogsci2010/papers/0508/paper0508.pdf}, booktitle = {Proceedings of the 32nd Annual Meeting of the Cognitive Science Society (CogSci 2010)} } @inproceedings{ubo:26019, author = {Tomanek, Katrin and Hahn, Udo and Lohmann, Steffen and Ziegler, Jürgen}, editor = {Linguistics, Association for Computational}, chapter = {}, title = {A Cognitive Cost Model of Annotations Based on Eye-Tracking Data}, year = {2010}, publisher = {ACL}, address = {Uppsala}, abstract = {We report on an experiment where the decision behavior of annotators issuing linguistic metadata is observed with an eyetracking device. As experimental conditions we consider the role of textual context and linguistic complexity classes. Still preliminary in nature, our data suggests that semantic complexity is much harder to deal with than syntactic one, and that full-scale textual context is negligible for annotation, with the exception of semantic high-complexity cases. We claim that such observational data might lay the foundation for empirically grounded annotation cost models and the design of cognitively adequate annotation user interfaces.}, isbn = {978-1-932432-66-4}, url = {http://www.aclweb.org/anthology-new/P/P10/P10-1118.pdf}, booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL 2010)} } @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:22921, author = {Lohmann, Steffen and Ziegler, Jürgen and Tetzlaff, Lena}, editor = {Wandke, Hartmut and Kain, Saskia and Struve, Doreen}, chapter = {}, title = {Ein Blick in die Wolken: Visuelle Exploration von Tag Clouds}, year = {2009}, publisher = {Oldenbourg}, address = {München}, isbn = {978-3-486-59222-1}, booktitle = {Mensch & Computer 2009} } @inproceedings{ubo:22500, author = {Lohmann, Steffen and Ziegler, Jürgen and Tetzlaff, Lena}, editor = {Gross, Tom and Gulliksen, Jan and Kotzé, Paula and Oestreicher, Lars and Palanque, Philippe and Prates, Oliveira Raquel and Winkler, Marco}, chapter = {}, title = {Comparison of Tag Cloud Layouts: Task-Related Performance and Visual Exploration}, year = {2009}, publisher = {Springer}, address = {Berlin, Heidelberg}, abstract = {Tag clouds have become a popular visualization and navigation interface on the Web. Despite their popularity, little is known about tag cloud perception and performance with respect to different user goals. This paper presents results from a comparative study of several tag cloud layouts. The results show differences in task performance, leading to the conclusion that interface designers should carefully select the appropriate tag cloud layout according to the expected user goals. Furthermore, the analysis of eye tracking data provides insights into the visual exploration strategies of tag cloud users.}, isbn = {978-3-642-03654-5}, url = {http://www.uni-due.de/ s400268/Lohmann09-interact.pdf}, booktitle = {Human-Computer Interaction - INTERACT 2009} } @inproceedings{ubo:18188, author = {El Jerroudi, Zoulfa and Ziegler, Jürgen and Meissner, Stephan and Philipsenburg, Axel}, editor = {Stary, C.}, chapter = {}, title = {E-Quest: Ein Online-Befragungswerkzeug für Web Usability}, year = {2005}, publisher = {Oldenbourg Verlag}, address = {München}, abstract = {E-Quest ist ein Werkzeug zur automatisierten Online-Befragungen. Es bietet ohne großen Konfigurationsaufwand die Möglichkeit zur komfortablen Gestaltung der Fragebögen und vielfältigen Auswertungsmöglichkeiten, um die Usability einer Webseite zu evaluieren.}, booktitle = {Mensch & Computer 2005: Kunst und Wissenschaft - Grenzüberschreitungen der interaktiven ART} } @article{ubo:14829, author = {Bullinger, h.J. and Heidmann, F. and Ziegler, J.}, title = {Usability Engineering für web-basierte Applikationen}, journal = {it+ti Informationstechnik und Technische Informatik}, year = {2002}, volume = {44}, number = {1}, pages = {5–13} } @article{ubo:14828, author = {Bullinger, H.J. and Ziegler, J. and Bauer, W.}, title = {Intuitive Human-Computer Interaction - Towards a User-Friendly Information Society}, journal = {International Journal of Human-Computer Interaction}, year = {2002}, volume = {14}, number = {1}, pages = {1–23} } @article{ubo:14818, author = {Bullinger, H.J. and Heidmann, F. and Ziegler, J.}, title = {Usability Engineering für Web-basierte Applikationen}, journal = {it+ti - Informationstechnik und technische Informatik}, year = {2001} } @inproceedings{ubo:14825, author = {Wissen, M. and Ziegler, J.}, editor = {Smith, M. Salvendy, G.}, chapter = {}, title = {Creativity support in system and process design}, year = {2001}, publisher = {Lawrence Erlbaum}, booktitle = {Proceedings of the 9th Int. Conf. on Human-Computer Interaction (HCI International 2001), Vol. 2: Systems, Social and Internationalization Aspects of Human-Computer Interaction New Orleans, USA Aug. 5-10, 2001; Proceedings of the 9th Int. Conf. on Hu} } @inproceedings{ubo:14819, author = {Ziegler, J.}, editor = {Stephanidis, C.}, chapter = {}, title = {Can standards and guidelines promote Universal Access?}, year = {2001}, publisher = {Lawrence Erlbaum}, booktitle = {Proceedings of the 9th Int. Conf. on Human-Computer Interaction (HCI International 2001), Vol. 3: Universal Access in HCI New Orleans, USA Aug. 5-10, 2001; Proceedings of the 9th Int. Conf. on Human-Computer Interaction (HCI International 2001), Vol.} } @inproceedings{ubo:14813, author = {Ziegler, J.}, editor = {Bullinger, H.J. and Ziegler, J.}, chapter = {}, title = {Standards for multimedia user interfaces - opportunities and issues}, year = {1999}, publisher = {Lawrence Erlbaum Associates}, booktitle = {Human-Computer Interaction - Communication, Cooperation and Application Design, Proceedings 8th Int. Conf. on Human-Computer Interaction, Vol. 2 Munich Aug. 22-26, 1999; Human-Computer Interaction - Communication, Cooperation and Application Design,} } @inproceedings{ubo:14816, author = {Bullinger, H.-J. and Ziegler, J.}, chapter = {}, title = {Human-Computer Interaction: Ergonomics and User Interfaces}, year = {1999}, publisher = {Lawrence Erlbaum Associates}, booktitle = {Vol. 1 of the Proceedings of the 8th International Conference on Human-Computer Interaction (HCI International ’99) Munich, Germany August 22-26, 1999; Vol. 1 of the Proceedings of the 8th International Conference on Human-Computer Interaction (HCI I} } @inproceedings{ubo:14809, author = {Sutcliffe, A.G. and Ziegler, J. and Johnson, P.}, chapter = {}, year = {1998}, publisher = {Kluwer Academic Publishers}, booktitle = {}, title = {Designing Effective and Usable Multimedia Systems} } @inproceedings{ubo:14805, author = {Ziegler, J.}, editor = {Tucker, A.B.J.}, chapter = {}, year = {1997}, publisher = {CRC Press}, booktitle = {}, title = {Interactive Techniques} } @inproceedings{ubo:14801, author = {Groh, G. and Ziegler, J. and Fähnrich, K.P.}, editor = {Fähnrich, K.P. and Janssen, C. and Groh, G.}, chapter = {}, year = {1996}, publisher = {Oldenbourg}, booktitle = {}, title = {Prototyping als Vorgehenweise zur GUI-Entwicklung} } @inproceedings{ubo:14802, author = {Janssen, C. and Ziegler, J.}, editor = {Fähnrich, K.P. and Janssen, C. and Groh, G.}, chapter = {}, year = {1996}, publisher = {Oldenbourg}, booktitle = {}, title = {Objektorientierter Entwurf graphischer Benutzungsschnittstellen} } @inproceedings{ubo:14803, author = {Ziegler, J. and Janssen, C. and Weisbecker, A.}, chapter = {}, title = {Automatische Generierung graphischer Benutzungsschnittstellen}, year = {1996}, publisher = {R.Oldenbourg Verlag Müchen Wien 1996}, booktitle = {Werkzeuge zur Entwicklung graphischer Benuterschrittstellen; Werkzeuge zur Entwicklung graphischer Benuterschrittstellen} } @book{ubo:14800, author = {Ziegler, J. and Ilg, R.}, title = {Benutzergerechte Software-Gestaltung}, year = {1993}, publisher = {Oldenbourg} }