@inproceedings{ubo_mods_00166333, author = {Herrmanny, Katja and Torkamaan, Helma}, editor = {Masthoff, Judith and Herder, Eelco and Tintarev, Nava and Tkalčič, Marko}, title = {Towards a User Integration Framework for Personal Health Decision Support and Recommender Systems}, booktitle = {Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization}, year = {2021}, publisher = {Association for Computing Machinery}, address = {New York}, pages = {65–76}, keywords = {Decision support system; Design framwork; Health recommender systems; User in the loop; User integration}, isbn = {978-1-4503-8366-0}, doi = {10.1145/3450613.3456816}, url = {https://doi.org/10.1145/3450613.3456816}, language = {en} } @inproceedings{ubo:67231, author = {Herrmanny, Katja and Ziegler, Jürgen and Dogangün, Aysegül and PERSUASIVE 2016}, editor = {Meschtscherjakov, Alexander and De Ruyter, Boris and Fuchsberger, Verena and Murer, Martin and Tscheligi, Manfred}, chapter = {}, title = {Supporting users in setting effective goals in activity tracking}, series = {Lecture notes in computer science}, year = {2016}, publisher = {Springer International Publishing}, address = {Cham}, volume = {9638}, pages = {15–26}, isbn = {978-3-319-31509-6}, doi = {10.1007/978-3-319-31510-2_2}, booktitle = {Persuasive Technology: 11th International Conference ; PERSUASIVE 2016 ; Salzburg, Austria, April 5-7, 2016 ; Proceedings} } @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: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} }