@article{ubo_mods_00204805, author = {Hernandez-Bocanegra, Diana C. and Ziegler, Jürgen}, title = {Explaining Recommendations through Conversations: Dialog Model and the Effects of Interface Type and Degree of Interactivity}, journal = {ACM Transactions on Interactive Intelligent Systems (TiiS)}, year = {2023}, publisher = {Association for Computing Machinery (ACM)}, address = {New York}, volume = {13}, number = {2}, keywords = {Recommender systems; explanations; argumentation; interactive interfaces; conversational agent; dataset; intent detection; user study}, issn = {2160-6455}, doi = {10.1145/3579541}, url = {https://doi.org/10.1145/3579541}, note = {001018513000001}, language = {en} } @inproceedings{kleemann2023, abstract = {Today’s e-commerce websites often provide many different components, such as filters and conversational product advisors, to help users find relevant items. However, filters and advisors are often presented separately and treated as independent entities so that the previous input is discarded when users switch between them. This leads to memory loads and disruptions during the search process. In addition, the reasoning behind the advisors’ results is often not transparent. To overcome these limitations, we propose a novel approach that exploits a graph structure to create an integrated system that allows a seamless coupling between filters and advisors. The integrated system utilizes the graph to suggest appropriate filter values and items based on the user’s answers in the advisor. Moreover, it determines follow-up questions based on the filter values set by the user. The interface visualizes and explains the relationship between a given answer and its relevant features to achieve increased transparency in the guidance process. We report the results of an empirical user study with 120 participants that compares the integrated system to a system in which the filtering and advisory mechanisms operate separately. The findings indicate that displaying recommendations and explanations directly in the filter component can increase acceptance and trust in the system. Similarly, combining the advisor with the filters along with the displayed explanations leads to significantly higher levels of knowledge about the relevant product features.}, address = {Cham}, author = {Kleemann, Timm and Ziegler, Jürgen}, series = {Lecture Notes in Computer Science}, booktitle = {Human-Computer Interaction – INTERACT 2023 : 19th IFIP TC13 International Conference, York, UK, August 28 – September 1, 2023, Proceedings, Part III}, editor = {Abdelnour Nocera, José and Kristı́n Lárusdóttir, Marta and Petrie, Helen and Piccinno, Antonio and Winckler, Marco}, isbn = {9783031422850}, issn = {0302-9743}, volume = {14144}, doi = {10.1007/978-3-031-42286-7_8}, pages = {137–159}, publisher = {Springer Nature Switzerland}, title = {Blending Conversational Product Advisors and Faceted Filtering in a Graph-Based Approach}, url = {https://doi.org/10.1007/978-3-031-42286-7_8}, note = {10.1007/978-3-031-42286-7_8}, language = {en}, keywords = {Search interfaces; explanations; knowledge graph}, year = {2023}, month = {aug}, day = {25}, month_numeric = {8} } @inproceedings{ubo_mods_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:74508, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, editor = {Yuizono, Takaya and Ogata, Hiroaki and Hoppe, Ulrich Heinz and Vassileva, Julita}, chapter = {}, title = {Hootle+: A Group Recommender System Supporting Preference Negotiation}, series = {Lecture Notes in Computer Science}, year = {2016}, publisher = {Springer}, address = {Cham}, volume = {9848}, pages = {151–166}, keywords = {Decision-Making}, isbn = {978-3-319-44799-5}, doi = {10.1007/978-3-319-44799-5_12}, url = {https://link.springer.com/chapter/10.1007/978-3-319-44799-5_12}, abstract = {This paper presents an approach to group recommender systems that focuses its attention on the group’s social interaction during the formulation, discussion and negotiation of the features the item to be jointly selected should possess. The system supports a collaborative preference elicitation and negotiation process where desired item features can be defined individually, but group consensus is needed for them to become active in the item filtering process. Users can provide feedback on other members’ preferences and change their significance, bringing up new recommendations each time individual settings are modified. The last stage in the decision process is also supported, when users collectively select the final item from the recommendation set. We developed the prototype hotel recommender Hootle+ and evaluated it in a user study involving groups of different size. The results indicate a good overall satisfaction, which increases with group size. However, the success ratio for bigger groups is lower than for small groups, raising questions for follow-up research. }, booktitle = {Collaboration and Technology: 22nd International Conference, CRIWG 2016, Kanazawa, Japan, September 14-16, 2016, Proceedings} } @inproceedings{ubo: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: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)} }