@inproceedings{10.1007/978-3-319-53676-7_2, author = {Jannach, Dietmar and Naveed, Sidra and Jugovac, Michael}, editor = {Bridge, Derek and Stuckenschmidt, Heiner}, title = {User Control in Recommender Systems: Overview and Interaction Challenges}, booktitle = {E-Commerce and Web Technologies}, year = {2017}, publisher = {Springer International Publishing}, pages = {21–33}, abstract = {Recommender systems have shown to be valuable tools that help users find items of interest in situations of information overload. These systems usually predict the relevance of each item for the individual user based on their past preferences and their observed behavior. If the system’s assumption about the users’ preferences are however incorrect or outdated, mechanisms should be provided that put the user into control of the recommendations, e.g., by letting them specify their preferences explicitly or by allowing them to give feedback on the recommendations. In this paper we review and classify the different approaches from the research literature of putting the users into active control of what is recommended. We highlight the challenges related to the design of the corresponding user interaction mechanisms and finally present the results of a survey-based study in which we gathered user feedback on the implemented user control features on Amazon.} } @inproceedings{ubo_mods_00108262, author = {Schäfer, Hanna and Hors-Fraile, Santiago and Karumur, Pavan Raghav and Valdez, Calero André and Said, Alan and Torkamaan, Helma and Ulmer, Tom and Trattner, Christoph}, title = {Towards Health (Aware) Recommender Systems}, booktitle = {Proceedings of the 2017 International Conference on Digital Health}, year = {2017}, publisher = {ACM}, address = {New York}, pages = {157–161}, keywords = {Patient modeling}, isbn = {978-1-4503-5249-9}, doi = {10.1145/3079452.3079499} } @inproceedings{ubo_mods_00106644, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, editor = {}, title = {Improving the Shopping Experience with an Augmented Reality-Enhanced Shelf}, booktitle = {Mensch und Computer 2017 - Workshopband}, year = {2017}, pages = {629–632}, keywords = {augmented reality; enhanced retailing; human-computer interaction}, doi = {10.18420/muc2017-demo-0351}, language = {en} } @inproceedings{ubo_mods_00094630, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Boratto, Ludovico and Carta, Salvatore and Fenu, Gianni}, chapter = {}, title = {Towards a Design Space for Personalizing the Presentation of Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1945}, pages = {10–17}, keywords = {Interactive control}, url = {http://ceur-ws.org/Vol-1945/paper_3.pdf}, abstract = {Although personalization plays a major role in the development of recommender systems, the presentation of recommendations and especially the way in which it can be adapted to suit the user’s needs has received relatively little attention from the research community. We introduce a design space for personalizing the presentation of recommendations and propose several dimensions that should be a part of it. Moreover, we present our initial insights about possible interactive mechanisms as well as potential evaluation criteria. Our goal is to provide a systematic way of designing personalized recommendation content, which should prove benecial for other researchers working on this topic. In the longer term, we are interested to investigate whether such personalized presentation implementations influence the perceived trustworthiness of the recommendations.}, booktitle = {EnCHIReS 2017: Engineering Computer-Human Interaction in Recommender Systems : Proceedings of the Second Workshop on Engineering Computer-Human Interaction in Recommender Systems co-located with the 9th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2017)} } @inproceedings{ubo_mods_00090488, author = {Donkers, Tim and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {Sequential User-based Recurrent Neural Network Recommendations}, year = {2017}, publisher = {ACM}, address = {New York, NY, USA}, pages = {152–160}, keywords = {Sequential Recommendations}, doi = {10.1145/3109859.3109877}, url = {https://dl.acm.org/doi/10.1145/3109859.3109877?cid=87958660357}, abstract = {Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network.}, booktitle = {Proceedings of the 11th ACM Conference on Recommender Systems (RecSys ’17)} } @inproceedings{ubo_mods_00090298, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Neidhardt, Julia and Fesenmaier, Daniel and Kuflik, Tsvi and Wörndl, Wolfgang}, chapter = {}, title = {Co-Staying: a Social Network for Increasing the Trustworthiness of Hotel Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1906}, pages = {35–39}, keywords = {Trustworthiness}, abstract = {Recommender systems attempt to match users’ preferences with items. To achieve this, they typically store and process a large amount of user profiles, item attributes, as well as an ever-increasing volume of user-generated feedback about those items. By mining user-generated data, such as reviews, a complex network consisting of users, items, and item properties can be created. Exploiting this network could allow a recommender system to identify, with greater accuracy, items that users are likely to find attractive based on the attributes mentioned in their past reviews as well as in those left by similar users. At the same time, allowing users to visualize and explore the network could lead to novel ways of interacting with recommender systems and might play a role in increasing the trustworthiness of recommendations. We report on a conceptual model for a multimode network for hotel recommendations and discuss potential interactive mechanisms that might be employed for visualizing it.}, url = {http://ceur-ws.org/Vol-1906/paper6.pdf}, booktitle = {RecTour 2017: 2nd Workshop on Recommenders in Tourism : Proceedings of the 2nd Workshop on Recommenders in Tourism co-located with 11th ACM Conference on Recommender Systems (RecSys 2017) Como, Italy, August 27, 2017} } @inproceedings{ubo_mods_00090297, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Domonkos, Tikk and Pu, Pearl}, chapter = {}, title = {Users’ Choices About Hotel Booking: Cues for Personalizing the Presentation of Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1905}, pages = {44–45}, keywords = {Tourism}, abstract = {Personalization in recommender systems has typically been applied to the underlying algorithms. In contrast, the presentation of individual recommendations—specifically, the various ways in which it can be adapted to suit the user’s needs in a more effective manner—has received relatively little attention by comparison. We present the results of an exploratory survey about users’ choices regarding hotel recommendations and draw preliminary conclusions about whether these choices can influence the presentation of recommendations.}, url = {http://ceur-ws.org/Vol-1905/recsys2017_poster22.pdf}, booktitle = {Poster Proceeding of ACM Recsys 2017: Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems (RecSys 2017) Como, Italy, August 28, 2017} } @inproceedings{ubo_mods_00089204, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Brusilovsky, Peter and de Gemmis, Marco and Felfernig, Alexander and Lops, Pasquale and O’Donovan, John and Tintarev, Nava and Willemsen, C. Martijn}, chapter = {}, title = {User Model Dimensions for Personalizing the Presentation of Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1884}, pages = {20–23}, keywords = {User profile}, abstract = {Personalization in recommender systems has typically been applied to the underlying algorithms and to the predicted result sets. Meanwhile, the presentation of individual recommendations—specifically, the various ways in which it can be adapted to suit the user’s needs in a more effective manner—has received relatively little attention by comparison. A limiting factor for the design of such interactive and personalized presentations is the quality of the user data, such as elicited preferences, that is available to the recommender system. At the same time, many of the existing user models are not optimized sufficiently for this specific type of personalization. We present the results of an exploratory survey about users’ choices regarding the presentation of hotel recommendations. Based on our analysis, we propose several novel dimensions to the conventional user models exploited by recommender systems. We argue that augmenting user profiles with this range of information would facilitate the development of more interactive mechanisms for personalizing the presentation of recommendations. This, in turn, could lead to increased transparency and control over the recommendation process.}, url = {http://ceur-ws.org/Vol-1884/paper4.pdf}, booktitle = {IntRS 2017: Interfaces and Human Decision Making for Recommender Systems : Proceedings of the 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2017)} } @inproceedings{ubo_mods_00089097, author = {Feuerbach, Jan and Loepp, Benedikt and Barbu, Catalin-Mihai and Ziegler, Jürgen}, title = {Enhancing an Interactive Recommendation System with Review-based Information Filtering}, booktitle = {Proceedings of the 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS ’17)}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1884}, pages = {2–9}, keywords = {User Reviews}, abstract = {Integrating interactive faceted filtering with intelligent recommendation techniques has shown to be a promising means for increasing user control in Recommender Systems. In this paper, we extend the concept of blended recommending by automatically extracting meaningful facets from social media by means of Natural Language Processing. Concretely, we allow users to influence the recommendations by selecting facet values and weighting them based on information other users provided in their reviews. We conducted a user study with an interactive recommender implemented in the hotel domain. This evaluation shows that users are consequently able to find items fitting interests that are typically difficult to take into account when only structured content data is available. For instance, the extracted facets representing the opinions of hotel visitors make it possible to effectively search for hotels with comfortable beds or that are located in quiet surroundings without having to read the user reviews.}, url = {http://ceur-ws.org/Vol-1884/paper1.pdf} } @inproceedings{ubo:80746, author = {Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {On User Awareness in Model-Based Collaborative Filtering Systems}, year = {2017}, keywords = {User Experience}, url = {https://iuiaware2017.files.wordpress.com/2016/11/on_user_awareness_in_model-based_collaborative_filtering_systems2.pdf}, abstract = {In this paper, we discuss several aspects that users are typically not fully aware of when using model-based Collaborative Filtering systems. For instance, the methods prevalently used in conventional recommenders infer abstract models that are opaque to users, making it difficult to understand the learned profile, and consequently, why certain items are recommended. Further, users are not able to keep an overview of the item space, and thus the alternatives that in principle could also be suggested. By summarizing our experiences on exploiting latent factor models for increasing control and transparency, we show that the respective techniques may also contribute to make users more aware of their preferences’ representation, the rationale behind the results, and further items of potential interest.}, booktitle = {Proceedings of the 1st Workshop on Awareness Interfaces and Interactions (AWARE ’17)} } @inproceedings{ubo:80745, author = {Kunkel, Johannes and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering}, year = {2017}, publisher = {ACM}, address = {New York, NY, USA}, pages = {3–15}, keywords = {3D Visualizations}, doi = {10.1145/3025171.3025189}, url = {https://dl.acm.org/doi/10.1145/3025171.3025189?cid=87958660357}, abstract = {While conventional Recommender Systems perform well in automatically generating personalized suggestions, it is often difficult for users to understand why certain items are recommended and which parts of the item space are covered by the recommendations. Also, the available means to influence the process of generating results are usually very limited. To alleviate these problems, we suggest a 3D map-based visualization of the entire item space in which we position and present sample items along with recommendations. The map is produced by mapping latent factors obtained from Collaborative Filtering data onto a 2D surface through Multidimensional Scaling. Then, areas that contain items relevant with respect to the current user’s preferences are shown as elevations on the map, areas of low interest as valleys. In addition to the presentation of his or her preferences, the user may interactively manipulate the underlying profile by raising or lowering parts of the landscape, also at cold-start. Each change may lead to an immediate update of the recommendations. Using a demonstrator, we conducted a user study that, among others, yielded promising results regarding the usefulness of our approach.}, booktitle = {Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI ’17)} }