@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{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.} } @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_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} } @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_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_00166667, author = {Hernandez Bocanegra, Diana Carolina and Ziegler, Jürgen}, title = {Assessing the Helpfulness of Review Content for Explaining Recommendations}, booktitle = {EARS 2019: The 2nd International Workshop on ExplainAble Recommendation and Search}, year = {2019}, publisher = {ACM}, address = {New York}, keywords = {Recommender systems, explanations}, url = {http://arxiv.org/abs/2010.06328}, archiveprefix = {arXiv}, eprint = {2010.06328}, language = {en} } @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_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_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_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_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_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_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} } @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{ubo_mods_00111873, author = {Stegemann, Timo and Ziegler, Jürgen}, editor = {Nikitina, Nadeschda and Song, Dezhao}, title = {Pattern-based analysis of SPARQL queries from the LSQ dataset}, booktitle = {Proceedings of the ISWC 2017 Posters & Demonstrations and Industry Tracks}, series = {CEUR Workshop Proceedings}, year = {2017}, publisher = {CEUR-WS}, address = {Aachen}, volume = {1963}, issn = {1613-0073}, url = {http://ceur-ws.org/Vol-1963/paper542.pdf} } @inproceedings{Stegemann2017, author = {Stegemann, Timo and Ziegler, Jürgen}, editor = {d’Amato, Claudia and Fernandez, Miriam and Tamma, Valentina and Lecue, Freddy and Cudré-Mauroux, Philippe and Sequeda, Juan and Lange, Christoph and Heflin, Jeff}, title = {Investigating Learnability, User Performance, and Preferences of the Path Query Language SemwidgQL Compared to SPARQL}, booktitle = {The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I}, year = {2017}, publisher = {Springer International Publishing}, address = {Cham}, pages = {611–627}, abstract = {In this paper, we present an empirical comparison of user performance and perceived usability for Sparql versus SemwidgQL, a path-oriented Rdf query language. We developed SemwidgQL to facilitate the formulation of Rdf queries and to enable non-specialist developers and web authors to integrate Linked Data and other semantic data sources into standard web applications. We performed a user study in which participants wrote a set of queries in both languages. We measured both objective performance as well as subjective responses to a set of questionnaire items. Results indicate that SemwidgQL is easier to learn, more efficient, and preferred by learners. To assess the applicability of SemwidgQL in real applications, we analyzed its expressiveness based on a large corpus of observed Sparql queries, showing that the language covers more than 90% of the typical queries performed on Linked Data.}, isbn = {978-3-319-68288-4}, doi = {10.1007/978-3-319-68288-4_36}, url = {https://doi.org/10.1007/978-3-319-68288-4_36} } @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_00092173, author = {Biefang, Kai and Kunkel, Johannes and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {Eine Sandbox zur physisch-virtuellen Exploration von Ausgrabungsstätten}, year = {2017}, publisher = {Gesellschaft für Informatik}, keywords = {Archäologie}, doi = {10.18420/muc2017-demo-0300}, url = {https://dl.gi.de/handle/20.500.12116/3248}, abstract = {In diesem Beitrag stellen wir die Archäologische Sandbox vor: Ein Tangible User Interface (TUI) mit dem archäologische Ausgrabungsstätten und dort gefundene Artefakte exploriert werden können. Das System zielt auf den Einsatz in Museen ab, die ihren Besuchern den Zusammenhang von ausgestellten Exponaten und der Ausgrabungsstätte näherbringen möchten, an der diese gefunden wurden. Den Kern des TUIs bildet eine mit Sand gefüllte Box, auf dessen Oberfläche eine geografische Karte projiziert wird. Durch das Graben im Sand an der richtigen Stelle werden Informationen zu an diesem Ort gefundenen Ausstellungsstücken abgerufen. Eine durchgeführte qualitative Interviewstudie bestätigt die intuitive Bedienbarkeit und die intrinsisch motivierenden Interaktionsmöglichkeiten des Systems.}, booktitle = {Mensch und Computer 2017 – Workshopband} } @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} } @incollection{ubo_mods_00084449, author = {Loepp, Benedikt and Ziegler, Jürgen}, editor = {Proff, Heike and Fojcik, Martin Thomas}, title = {Empirische Bedarfsanalyse zur intermodalen Navigation und dem Einsatz von Informationssystemen zur Förderung ihrer Attraktivität}, booktitle = {Innovative Produkte und Dienstleistungen in der Mobilität: Technische und betriebswirtschaftliche Aspekte}, year = {2017}, publisher = {Springer Gabler}, address = {Wiesbaden, Germany}, pages = {409–426}, keywords = {Intermodale Navigation}, isbn = {978-3-658-18613-5}, doi = {10.1007/978-3-658-18613-5_26}, url = {http://dx.doi.org/10.1007/978-3-658-18613-5_26}, abstract = {Eine vermehrt intermodale Fortbewegung ist in Zeiten, in denen angesichts überfüllter Innenstädte und Straßen die Verringerung des Verkehrsaufkommens und Verlagerung des motorisierten Individualverkehrs auf umweltfreundlichere Alternativen zunehmend relevanter werden, von großer Bedeutung. Heute gängige Navigationslösungen und Routenplaner sind bezüglich der Unterstützung komplexer, insbesondere intermodaler Mobilitätsketten jedoch oft nur unzureichend entwickelt: Es mangelt einerseits an einer einheitlichen Integration der Informationen unterschiedlicher Verkehrsmittel, andererseits vor allem an Personalisierungsmöglichkeiten und einer intelligenten Anpassung der Systeme an die momentane Situation des Nutzers. Oft werden beispielsweise Angebote wie Car- und Bikesharing noch außer Acht gelassen, und auch der Kontext als wichtige Determinante bei der Wahl einer Route – für einen beruflichen Termin bei schlechter Witterung kann ein Fahrrad etwa weniger geeignet sein als bei einem Familienausflug unter sonnigen Bedingungen – bleibt meist unberücksichtigt. In diesem Beitrag stellen wir die Ergebnisse einer im Rahmen des BMVI-geförderten Projekts colognE-mobil II durchgeführten Bedarfsanalyse bestehend aus zwei empirischen Untersuchungen vor: Sowohl online als auch vor Ort im Rhein-Ruhr-Gebiet wurden 318 bzw. 130 Personen befragt, um die aktuelle Nutzung unterschiedlicher Verkehrsträger zu untersuchen, sowie die Einschätzung ihrer bestehenden Probleme und künftigen Potenziale zu ermitteln. Spezieller Fokus lag auf den gerade für den städtischen Raum relevanten Alternativen, etwa Car- und Bikesharing, aber auch intermodaler Navigation im Allgemeinen. Während sich grundsätzlich eine hohe Nutzungsbereitschaft abzeichnete, leiden derartige Angebote aus Sicht der Befragten u. a. an Zugangsschwierigkeiten, mangelnder Verbreitung, oder waren schlicht zu unbekannt, als dass sie eine echte Alternative hätten darstellen können. Die Auswahl an Verkehrsmitteln wird unterdessen beständig größer, und beinhaltet zunehmend ökologischere Varianten als den Individualverkehr. Aufgrund steigender Kraftstoffpreise, CO2-Abgaben, Straßenmaut und überlasteten Innenstädten erhöht sich dementsprechend die Bedeutung der Unabhängigkeit vom privaten PKW, vor allem bei Jüngeren. Mit Hilfe von Mock-Ups eines Routenplaners untersuchten wir deshalb ebenfalls, ob sich Nutzer mittels geeigneter Hinweise – etwa auf erhöhte Umweltfreundlichkeit oder die aktuelle Verkehrslage – dazu animieren lassen, ihre typischerweise routinierte Strecken- und Verkehrsmittelwahl anzupassen. Die Ergebnisse zeigen u. a., dass zur Assistenz bei Fahrplanauskunft oder Routenplanung entwickelte Informationssysteme auf diese Weise die wahrgenommene Attraktivität intermodaler Wegeketten sowie von Car- und Bikesharing-Angeboten signifikant steigern können.} } @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: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: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:69423, author = {Gaulke, Werner and Ziegler, Jürgen}, chapter = {}, title = {Rule-enhanced task models for increased expressiveness and compactness}, year = {2016}, edition = {EICS ’16}, publisher = {ACM}, address = {Brussels, Belgium}, pages = {4–15}, isbn = {978-1-4503-4322-0}, doi = {10.1145/2933242.2933243}, abstract = {User centered design and development of interactive systems utilizes theoretically well-grounded, yet practical ways to capture user’s goals and intentions. Task models are an established approach to break down a central objective into a set of hierarchical organized tasks. While task models achieve to provide a good overview of the overall system, they often lack detail necessary to (semi-) automatically generate user interfaces. Based on requirements derived from a comprehensive overview of existing task model extensions, improvements and development methods, an approach that integrates logical rules with task models is introduced. By means of practical examples it is shown, that the integration of rules enables advanced execution flows as well as leaner task models thus improving their practical value.}, booktitle = {Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems} } @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} } @inproceedings{ubo:54813, author = {Gaulke, Werner and Ziegler, Jürgen}, editor = {Ziegler, Jürgen and Nebeling, Michael and Laurence, Nigay}, chapter = {}, title = {Using profiled ontologies to leverage model driven user interface generation}, year = {2015}, publisher = {ACM}, address = {New York, NY, USA}, isbn = {978-1-4503-3646-8}, url = {http://dx.doi.org/10.1145/2774225.2775070}, abstract = {Mobile computing and new input methods have increased the need to create multiple interfaces for one functional core. Automatic generation of user interfaces attempts a solution for this problem. Existing approaches either generate interfaces on the base of a detailed task model or use domain models in conjunction with interface specific annotations and transformation rules. While task models are very time consuming to create and cannot easily be reused domain models lack the flexibility for use cases which are not covered or in conflict with used transformation rules. Based on an overview of existing approaches this paper sets out a conceptual framework which combines both task model and ontology based concepts. It is shown that the proposed combination leads to more abstract and reusable task models.}, booktitle = {Proceedings of the 7th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS ’15)} } @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)} } @inproceedings{ubo:48984, author = {Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {Komplexe Präferenzprofile für intermodale Navigation}, year = {2014}, publisher = {De Gruyter Oldenbourg}, address = {Berlin}, isbn = {978-3-11-034450-9}, doi = {10.1524/9783110344509.191}, url = {http://dx.doi.org/10.1524/9783110344509.191}, pages = {191–198}, abstract = {In diesem Beitrag präsentieren wir Aspekte, die für Nutzer im Kontext intermodaler Navigation von Bedeutung sind und durch komplexe Präferenzprofile abgebildet werden können. Neben einer Herleitung und Klassifikation solch potenzieller Nutzeranforderungen an die Anpassung von Mobilitätsketten − beispielsweise an individuelle Präferenzen wie den energetischen Fußabdruck oder das gewünschte Maß an Komfort – stellen wir einen Prototypen vor, der einen ersten Schritt hin zu einer interaktiven, stark individualisierbaren Unterstützung intermodaler Navigation darstellt. }, booktitle = {Mensch und Computer 2014 – Workshopband} } @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)} } @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:26213, author = {Joop, Björn and Ziegler, Jürgen}, chapter = {}, title = {Group context-based adaptations for recommendation}, year = {2010}, address = {Hong Kong, China}, abstract = {In groupware or community based applications the user interface is usually static or tailored to the individual user’s needs. Newer developments try to adapt the user interface automatically in regard to user contexts. Even though these techniques are proven useful, there exists no contextadaptive system taking the current context of a group or community in regard. In this paper, we briefly discuss the problems of defining context and present our understanding of context as a subset of the current information state. We provide an exemplary scenario to present different approaches how to compute group contexts based on semantic models and user contexts, and the consequences for the adaptation goals - in the interface or through changes at system functionalities or tools. We additionally discuss the problems occurring at evaluating adaptations and the value of group context for collaborative work.}, url = {http://duepublico.uni-duisburg-essen.de/servlets/DocumentServlet?id=22545}, booktitle = {Semantic Models for Adaptive Interactive Systems (SEMAIS), 1st Workshop in conjunction with the International Conference on Intelligent User Interfaces (IUI) 2010.} } @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:26214, author = {Joop, Björn and Ziegler, Jürgen}, editor = {Baloian, Nelson and Luther, Wolfram and Söffker, Dirk and Urano, Yoshiyori}, chapter = {}, title = {A framework for context-based adaptation (for collaboration)}, year = {2010}, address = {Berlin: Logos}, url = {http://duepublico.uni-duisburg-essen.de/servlets/DocumentServlet?id=22547}, abstract = {The topic of our talk focuses on definitions of context and approaches in computer science to model context in adaptive or context-aware systems. We present briefly an own context-understanding, a multi-layered framework for context-based adaptation. We present some current examples for context-based adaptation and conclude with some further thoughts about adding unstructured information such as tags to our context understanding to be able to mediate knowledge between different contexts and users.}, booktitle = {Interfaces and Interaction Design for Learning and Simulation Environments} } @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)} } @incollection{ubo_mods_00024885, author = {Hussein, Tim and Gaulke, Werner and Linder, Timm and Ziegler, Jürgen}, title = {Improving collaboration by using context views}, booktitle = {Context-Adaptive Interaction for Collaborative Work (CAICOLL), 1st Workshop in conjunction with the ACM Conference on Human Factors in Computing Systems (CHI) 2010}, year = {2010}, address = {Atlanta, GA, USA}, pages = {1–6}, url = {http://duepublico.uni-duisburg-essen.de/servlets/DocumentServlet?id=22051}, abstract = {In this paper, we explain our notion of context, considering for instance membership in a group as context. We derive a model for context-adaptivity from the well-established one for user-adaptivity proposed by Jameson, and introduce context views as means for facilitating group-based work. Context views aim at identifying the most important elements within an application in a generic way by exploiting context information.} } @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:23454, author = {Joop, Björn and Hussein, Tim and Ziegler, Jürgen}, chapter = {}, title = {Nutzung individueller Kontextinformationen zur Verbesserung von kollaborativen Arbeiten}, year = {2009}, address = {Berlin}, abstract = {Kollaborative Arbeitsprozesse werden entweder durch spezialisierte Arbeitsumgebungen gezielt unterstützt oder sind individualisierbar. Kontextbasierte Kollaborationsumgebungen können sich kontextabhängig auf die jeweilige Arbeitssituationen einstellen und so dynamisch auf Änderungen der Prozesse oder der Benutzer reagieren. In diesem Beitrag stellen wir ein Kontext-Modell vor, welches sowohl individuelle Nutzerkontexte, wie auch gemeinsamen Kontext multipler Benutzer, erfassen, repräsentieren und als Basis für Adaptionseffekte nutzen kann.}, url = {http://duepublico.uni-duisburg-essen.de/servlets/DocumentServlet?id=21069}, booktitle = {Open Design Spaces 2009} } @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)} } @incollection{ubo_mods_00019293, author = {Lohmann, Steffen and Ziegler, Jürgen}, editor = {Herczeg, Michael and Kindsmüller, Christoph}, title = {Webbasierte Erfassung und Analyse von Nutzeranforderungen}, booktitle = {Mensch und Computer 2008}, year = {2008}, publisher = {Oldenbourg}, address = {München}, pages = {419–422}, abstract = {Dieser Beitrag beschreibt die Anwendungen Softfox und SW-Analytics, die eine webbasierte Erfassung von Nutzeranforderungen und deren strukturierte Analyse ermöglichen. Ziel ist es, Endanwendern eine Möglichkeit zu geben, jederzeit und von jedem Standort aus Anforderungen auf Basis einer vorhandenen Webanwendung zu äußern.}, isbn = {978-3-486-58900-9}, url = {http://mc.informatik.uni-hamburg.de/konferenzbaende/mc2008/konferenzband/mc2008_47_lohmann.pdf} } @book{ubo:19693, editor = {Auer, Sören and Dietzold, Sebastian and Lohmann, Steffen and Ziegler, Jürgen}, title = {Proceedings of the International Workshop on Interacting with Multimedia Content in the Social Semantic Web (IMC-SSW’08)}, series = {CEUR-WS}, year = {2008}, address = {Aachen}, url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-417/} } @inproceedings{ubo:19282, author = {Heim, Philipp and Dalli, Deniz and Ziegler, Jürgen}, editor = {Herczeg, Michael and Kindsmüller, Christof Martin}, chapter = {}, title = {ListGraph: Visuelle Analyse von RDF-Daten}, year = {2008}, publisher = {Oldenburg}, address = {München}, isbn = {978-3-486-58900-9}, booktitle = {Mensch und Computer 2008} } @inproceedings{ubo:19284, author = {Lohmann, Steffen and Ziegler, Jürgen and Heim, Philipp}, editor = {Forbrig, Peter and Paternò, Fabio}, chapter = {}, title = {Involving End Users in Distributed Requirements Engineering}, year = {2008}, publisher = {Springer}, address = {Berlin, Heidelberg}, abstract = {Active involvement of end users in the development of interactive systems is both highly recommended and highly challenging. This is particularly true in settings where the requirements of a large number of geographically distributed users have to be taken into account. In this paper, we address this problem by introducing an integrated, web-based approach that enables users to easily express their ideas on how the interaction with a system could be improved. In addition, the user input is contextualized, allowing for highly structured means to access, explore, and analyze the user requirements.}, isbn = {9783540859918}, booktitle = {Engineering Interactive Systems 2008} } @inproceedings{ubo:19843, author = {El Jerroudi, Zoulfa and Ziegler, Jürgen}, editor = {Herczeg, Michael and Kindsmüller, Christoph}, chapter = {}, title = {Interaktive visuelle Analyse für die Zusammenführung von Ontologien}, year = {2008}, publisher = {Oldenbourg Verlag}, address = {München}, abstract = {In dieser Arbeit werden neue interaktive und visuelle Analysetechniken vorgestellt, die den kognitiven Prozess beim Vergleichen und Zusammenführen von Ontologien unterstützen. Der Schwerpunkt der Arbeit liegt auf der Nachvollziehbarkeit des Merging-Prozesses für den Nutzer und auf der Exploration von Vergleichsergebnissen durch dynamische Visualisierungskomponenten. Der hier vorgestellte Ontologie-Editor iMERGE erlaubt nicht nur die Erzeugung aussagekräftiger visueller Darstellungen, sondern ermöglicht auch ein hohes Maß an Interaktion. Dies bezieht sich sowohl auf die Steuerung der visuellen Repräsentation der Ontologie, als auch auf die Interaktion mit den Ergebnissen des Vergleichssprozesses.}, isbn = {978-3-486-58900-9}, booktitle = {Mensch und Computer 2008: Viel Mehr Interaktion} } @inproceedings{ubo:19846, author = {El Jerroudi, Zoulfa and Ziegler, Jürgen}, chapter = {}, title = {iMERGE: Interactive Ontology Merging}, year = {2008}, publisher = {Springer}, address = {Acitrezza, Italy}, abstract = {In this paper we present novel visual analytics techniques which help the user in the process of interactive ontology mapping and merging. A major contribution will be the strong integration and coupling of interactive visualizations with the merging process enabling the user to follow why concepts are merged and at which position in the ontology they are merged. For this purpose, adapted ontology similarity measures and new techniques for representing ontologies will be required to enable responsive, real time visualization and exploration of the comparing and merging results.}, booktitle = {EKAW 2008 – 16th International Conference on Knowledge Engineering and Knowledge Management Knowledge Patterns} } @inproceedings{ubo:19281, author = {Heim, Philipp and Ziegler, Jürgen}, editor = {Tochtermann, Klaus and Maurer, Hermann}, chapter = {}, title = {Handling the Complexity of RDF Data: Combining List and Graph Visualization}, year = {2008}, publisher = {J.UCS}, address = {Graz}, booktitle = {Proceedings of the 8th International Conference on Knowledge Management (I-KNOW 08)} } @inproceedings{ubo:19280, author = {Heim, Philipp and Lohmann, Steffen and Lauenroth, Kim and Ziegler, Jürgen}, chapter = {}, title = {Graph-based Visualization of Requirements Relationships}, year = {2008}, publisher = {IEEE}, address = {Los Alamitos, CA, USA}, abstract = {Understanding the relationships between require- ments is important in order to understand the requirements themselves. Existing requirements management tools mainly use lists, tables, trees, and matrices to visualize requirements and their interrelations. However, all these visualization forms have a limited capability to show multiple relationships of different types. In this paper, we propose to extend traditional requirements analysis and management by a graph-based visualization that allows to represent multidi- mensional relations in a direct and flexible way. In particular, we propose a special presentation form that enables the exploration of requirements along their relationships and facilitates understanding of dependencies between requirements.}, isbn = {978-0-7695-3629-3}, booktitle = {Proceedings of the 3rd International Workshop on Requirements Engineering Visualization (REV 08)} } @inproceedings{ubo:19692, author = {Heim, Philipp and Ziegler, Jürgen and Lohmann, Steffen}, editor = {Auer, Sören and Dietzold, Sebastian and Lohmann, Steffen and Ziegler, Jürgen}, chapter = {}, title = {gFacet: A Browser for the Web of Data}, year = {2008}, publisher = {CEUR-WS}, address = {Aachen}, abstract = {This paper introduces a new approach to browsing the Web of data by combining graph-based visualization with faceted filtering techniques. The graph-based visualization of facets supports the integration of different domains and an efficient exploration of highly structured and interrelated datasets. It allows to access information from distant user-defined perspectives and thereby enables the exploration beyond the borders of Web pages.}, url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-417/paper5.pdf}, booktitle = {Proceedings of the International Workshop on Interacting with Multimedia Content in the Social Semantic Web (IMC-SSW’08)} } @inproceedings{ubo:18143, author = {Lohmann, Steffen and Riechert, Thomas and Auer, Sören and Ziegler, Jürgen}, editor = {Maalej, Walid and Brügge, Bernd}, chapter = {}, title = {Collaborative Development of Knowledge Bases in Distributed Requirements Elicitation}, year = {2008}, publisher = {Köllen}, address = {Bonn}, abstract = {One of the main challenges in distributed software development is the elicitation and management of knowledge regarding system requirements. Due to spatial distribution of involved parties, many limitations concerning interaction, communication, and conceptualization have to be faced. The SoftWiki project aims to provide an agile, wiki-based methodology to overcome these limitations in part. This paper introduces the SoftWiki approach and presents some of the tools that are developed to support knowledge sharing in distributed requirements engineering.}, isbn = {3885792161}, booktitle = {Software Engineering 2008 - Workshopband} } @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:14861, author = {Lohmann, Steffen and Kaltz, Wolfgang J. and Ziegler, Jürgen}, editor = {Kühne, Thomas}, chapter = {}, title = {Model-Driven Dynamic Generation of Context-Adaptive Web User Interfaces}, year = {2007}, publisher = {Springer}, address = {Berlin, Heidelberg}, abstract = {The systematic development of user interfaces that enhance interaction quality by adapting to the context of use is a desirable, but also highly challenging task. This paper examines to which extent contextual knowledge can be systematically incorporated in the model-driven dynamic generation of Web user interfaces that provide interaction for operational features. Three parts of the generation process are distinguished: selection, parameterization, and presentation. A semantically enriched service-oriented approach is presented that is based on the Catwalk framework for model interpretation and generation of adaptive, context-aware Web applications. Automation possibilities are addressed and an exemplary case study is presented.}, isbn = {9783540694885}, booktitle = {Models in Software Engineering - Workshops and Symposia at MoDELS 2006} } @inproceedings{ubo:18191, editor = {Lang, Eike and Ziegler, Jürgen and Wissen, Michael and Lang, Eike and Ziegler, Jürgen and Wissen, Michael}, chapter = {}, title = {Modellgetriebene Generierung von Webanwendungsprototypen}, year = {2007}, publisher = {Oldenbourg}, address = {München}, abstract = {Existierende Entwicklungsmethoden und Werkzeuge für das Software-Engineering unterstützen die Entwicklung informationsgetriebener Webanwendungen nur unzureichend und finden in der Praxis daher kaum Anwendung. Im BMBF-geförderten Projekt ?Web Information and Service Engineering“ (WISE) entstand ein werkzeugunterstützter Methodenverbund, der speziell auf die Bedürfnisse bei der Entwicklung webbasierter Informationssysteme zugeschnitten ist. Die Modellierung erfolgt auf Basis von Ontologien und leicht verständlichen Navigationsmodellen. Ein grafischer Editor ermöglicht die Erstellung und Pflege der Modelle und übergibt diese an eine Generatorkomponente, die automatisiert funktionsfähige Webanwendungs-Prototypen erzeugt.}, isbn = {978-3486584967}, url = {http://mc.informatik.uni-hamburg.de/konferenzbaende/mc2007/konferenzband/mc2007_30_lang.pdf}, booktitle = {Mensch und Computer 2007: Interaktion im Plural} } @article{ubo:14862, author = {El Jerroudi, Z. and Ziegler, J.}, title = {Interaktives Vergleichen und Zusammenführen von Ontologien}, journal = {i-com: Zeitschrift für interaktive und kooperative Medien}, year = {2007}, volume = {6}, number = {3}, pages = {44–49}, abstract = {In dieser Arbeit werden semi-automatische Verfahren vorgestellt, die den Domänenexperten beim interaktiven Vergleichen und Zusammenführen von Ontologien unterstützen sollen. Das Ziel beim Vergleichen von zwei Ontologien besteht darin, eine Abbildung zwischen der Quell- und Zielontologie zu finden, die dem Nutzerverständnis einer semantischen Entsprechung am nächsten kommt. Für die Initialisierung des Vergleichsprozesses startet der iMERGING-Editor mit der linguistischen Ähnlichkeit von zwei Konzepten, danach werden die strukturellen Eigenschaften der Ontologie berücksichtigt und im dritten Schritt wird die Ähnlichkeit aufgrund zusätzlich vorhandener Informationen wie die Verknüpfung mit Dokumenten untersucht. Nutzereingaben, wie das Ablehnen oder Ändern eines Mappings, werden im Vergleichsprozess interaktiv berücksichtigt, so dass Domänenwissen des Nutzers mit einbezogen werden kann, um semantische Entsprechungen zwischen zwei Elementen der Wissensstruktur zu finden.}, issn = {1618-162X} } @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} } @inproceedings{ubo:18193, author = {Ziegler, Jürgen and Strobl, Armin and Wolsing, Ansgar and Mohr, Christina and Lotze, Rouven and Lang, Eike}, editor = {Heinecke, M. Andreas and Paul, Hansjürgen}, chapter = {}, title = {ARTierchen - Augmented Reality in Touch}, year = {2006}, publisher = {Oldenburg}, address = {München}, abstract = {Augmented Reality (AR) ist eine Technik, welche die reale Welt um virtuelle Objekte erweitert. Unter Nutzung von mobilen Geräten und speziellen AR-Displays können auch virtuelle Spielwelten in räum- liche Umgebungen eingebettet werden. Im vorliegenden Beitrag sollen am Beispiel des Augmented- Reality-Spiels ?ARTierchen“ die Möglichkeiten dargestellt werden, die sich dem Entwickler durch die computergerenderte visuelle Erweiterung einer zugrunde liegenden natürlichen Spielwelt für interakti- ve 3D-Spiele bieten.}, isbn = {978-3486581294}, url = {http://mc.informatik.uni-hamburg.de/konferenzbaende/mc2006/konferenzband/muc2006_46_strobl_etal.pdf}, booktitle = {Mensch und Computer 2006: Mensch und Computer im Strukturwandel} } @inproceedings{ubo:14850, author = {Ziegler, Jürgen and Lohmann, Steffen and Kaltz, Wolfgang J.}, editor = {Stary, Christian}, chapter = {}, title = {Kontextmodellierung für adaptive webbasierte Systeme}, year = {2005}, publisher = {Oldenbourg}, address = {München}, abstract = {Adaptive Web-Anwendungen erfordern die Berücksichtigung von Kontext zur Anpassung von Inhalten, Navigationsstrukturen und Präsentationsformen. Für eine systematische Entwicklung kontextadaptiver Systeme sind Methoden der Kontextmodellierung erforderlich, die die komplexen Abhängigkeiten beschreiben. In diesem Beitrag wird ein konzeptioneller Rahmen vorgestellt, der auf einer Taxonomie der verschiedenen Kontextaspekte basiert. Darüber hinaus werden unterschiedliche Mechanismen der Kontextualisierung diskutiert, die von probabilistischen Verfahren bis zu regelbasierten Techniken reichen.}, isbn = {3486578057}, url = {http://mc.informatik.uni-hamburg.de/konferenzbaende/mc2005/konferenzband/muc2005_16_ziegler_etal.pdf}, booktitle = {Mensch Computer 2005: Kunst und Wissenschaft - Grenzüberschreitungen der interaktiven ART} } @inproceedings{ubo:14853, author = {Ziegler, J. and El Jerroudi, Z. and Böhm, K.}, chapter = {}, title = {Generating semantic contexts from spoken conversation in meetings}, year = {2005}, publisher = {ACM}, address = {San Diego, California, USA}, isbn = {1-58113-894-6}, booktitle = {Proceedings of the 10th international conference on Intelligent user interfaces ( IUI 2005)} } @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:14852, author = {Kaltz, Wolfgang J. and Ziegler, Jürgen and Lohmann, Steffen}, title = {Context-aware Web Engineering: Modeling and Applications}, journal = {RIA - Revue d’Intelligence Artificielle, Special Issue on Applying Context-Management}, year = {2005}, volume = {19}, number = {3}, pages = {439–458}, abstract = {This article presents an approach to Web Engineering which aims to account for context-awareness in a comprehensive and integrated fashion, thus enabling an enhanced adaptation of the application to the end-user. A conceptual model, permitting the combination of a domain ontology with context-relevant parameters and a degree of relevance, is presented. Subsequently, the use of such a model in a Web Engineering process is discussed, including appropriate modeling software, and requirements for a runtime system.}, issn = {0992-499X} } @inproceedings{ubo:18190, author = {Ziegler, Jürgen and El Jerroudi, Zoulfa and Böhm, Carsten and Beinhauer, Wolfgang and Räther, Christian}, editor = {Keil-Slawik, R. and Selke, H. and Szwillus, G.}, chapter = {}, title = {Automatische Themenextraktion aus gesprochener Sprache}, year = {2004}, publisher = {Oldenbourg Verlag.}, address = {München}, abstract = {Bei vielen Formen der Kommunikation und Kooperation in Gruppensitzungen kann das Bereitstellen eines expliziten semantischen Kontextes wertvolle Unterstützung bieten. Semantische Kontexte können das gemeinsame Verständnis eines Problembereichs verbessern, die assoziative Ideenfindung unterstützen, eine moderierende Funktion in der Kommunikation übernehmen oder als Basis für Assistenzfunktionen genutzt werden. In diesem Beitrag wird ein Ansatz präsentiert, in dem semantische Kontexte aus gesprochener Konversation in Echtzeit gewonnen werden. Der vorgestellte Prototyp verknüpft Technologien zur Spracherkennung und zur Analyse unstrukturierter großer Textkorpora miteinander und extrahiert Themenstrukturen aus gesprochener Sprache durch semantisches Matching gegen Terminologiedatenbanken und Ontologien. Die Themenstrukturen werden graphisch visualisiert und in die Diskussion rückgekoppelt, um geeignete Unterstützungsfunktionen anbieten zu können.}, booktitle = {Mensch & Computer 2004: Allgegenwärtige Interaktion} }