@inproceedings{ubo_mods_00168064, author = {Hernandez-Bocanegra, Diana Carolina and Ziegler, Jürgen}, title = {ConvEx-DS: A Dataset for Conversational Explanations in Recommender Systems}, booktitle = {Interfaces and Human Decision Making for Recommender Systems 2021: Proceedings of the 8th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems}, series = {CEUR Workshop Proceedings}, year = {2021}, publisher = {CEUR-WS}, address = {Aachen}, volume = {2948}, pages = {3–20}, keywords = {Conversational agent; Dataset; Explanations; Recommender systems; User study}, issn = {1613-0073}, language = {en} } @inproceedings{ubo_mods_00167903, author = {Hernandez Bocanegra, Diana Carolina and Ziegler, Jürgen}, editor = {Ardito, Carmelo and Lanzilotti, Rosa and Malizia, Alessio and Petrie, Helen and Piccinno, Antonio and Desolda, Giuseppe and Inkpen, Kori}, title = {Effects of Interactivity and Presentation on Review-Based Explanations for Recommendations}, booktitle = {Human-Computer Interaction – INTERACT 2021: Proceedings, Part II}, series = {Lecture Notes in Computer Science}, year = {2021}, publisher = {Springer}, address = {Cham}, volume = {12933}, pages = {597–618}, keywords = {Explanations; Interactivity; Recommender systems; User characteristics; User study}, abstract = {User reviews have become an important source for recommending and explaining products or services. Particularly, providing explanations based on user reviews may improve users’ perception of a recommender system (RS). However, little is known about how review-based explanations can be effectively and efficiently presented to users of RS. We investigate the potential of interactive explanations in review-based RS in the domain of hotels, and propose an explanation scheme inspired by dialogue models and formal argument structures. Additionally, we also address the combined effect of interactivity and different presentation styles (i.e. using only text, a bar chart or a table), as well as the influence that different user characteristics might have on users’ perception of the system and its explanations. To such effect, we implemented a review-based RS using a matrix factorization explanatory method, and conducted a user study. Our results show that providing more interactive explanations in review-based RS has a significant positive influence on the perception of explanation quality, effectiveness and trust in the system by users, and that user characteristics such as rational decision-making style and social awareness also have a significant influence on this perception.}, isbn = {978-3-030-85615-1}, doi = {10.1007/978-3-030-85616-8_35}, url = {https://doi.org/10.1007/978-3-030-85616-8_35}, language = {en} } @inproceedings{ubo_mods_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} } @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} }