%0 Conference Paper %1 Suchmann2024 %A Suchmann, Lovis Bero %A Krämer, Nicole %A Ziegler, Jürgen %B Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization %C New York, NY, USA %D 2024 %I Association for Computing Machinery %K interactive-recommender umap2024 user-control %P 106–113 %R 10.1145/3631700.3664872 %T Dynamic Ridge Plot Sliders: Supporting Users' Understanding of the Item Space Structure and Feature Dependencies in Interactive Recommender Systems %U https://doi.org/10.1145/3631700.3664872 %X Understanding the characteristics of the space of available items is instrumental for successfully exploring the options in interactive recommender system approaches, including conversational recommender systems. Forming an adequate mental model of the distribution of items with respect to their various features can prevent mismatches with the user’s preferences, either represented in an existing preference model or, even more importantly, in a process of preference development during the interaction. However, such a mental model is limited due to complex and opaque feature dependencies manifested in the item space that make it hard to predict whether a combination of feature preferences is realistically satisfiable or should be partially relaxed. Illuminating these dependencies in the recommender interface while the user scrutinizes their preference model has the potential to convey richer structural information, facilitate mental model formation, and thus allow better-informed decisions for both preferred feature ranges and recommended items. To this end, we introduce novel dynamic ridge plot sliders that employ guidelines from research on information visualization for mental model formation. The proposed sliders relate feature-wise item distributions to items’ overall utility values and in doing so provide predictive power about the implications of future user actions and preference adjustments. Furthermore, paradigms for interactions between multiple such sliders and other GUI elements are motivated via an interactive recommender prototype. We also present a preliminary user study with yet inconclusive results, and discuss the future potentials and conceivable application areas of our approach, as well as promising next research steps. %@ 9798400704666

@inproceedings{Suchmann2024, abstract = {Understanding the characteristics of the space of available items is instrumental for successfully exploring the options in interactive recommender system approaches, including conversational recommender systems. Forming an adequate mental model of the distribution of items with respect to their various features can prevent mismatches with the user’s preferences, either represented in an existing preference model or, even more importantly, in a process of preference development during the interaction. However, such a mental model is limited due to complex and opaque feature dependencies manifested in the item space that make it hard to predict whether a combination of feature preferences is realistically satisfiable or should be partially relaxed. Illuminating these dependencies in the recommender interface while the user scrutinizes their preference model has the potential to convey richer structural information, facilitate mental model formation, and thus allow better-informed decisions for both preferred feature ranges and recommended items. To this end, we introduce novel dynamic ridge plot sliders that employ guidelines from research on information visualization for mental model formation. The proposed sliders relate feature-wise item distributions to items’ overall utility values and in doing so provide predictive power about the implications of future user actions and preference adjustments. Furthermore, paradigms for interactions between multiple such sliders and other GUI elements are motivated via an interactive recommender prototype. We also present a preliminary user study with yet inconclusive results, and discuss the future potentials and conceivable application areas of our approach, as well as promising next research steps.}, added-at = {2024-07-01T17:25:49.000+0200}, address = {New York, NY, USA}, author = {Suchmann, Lovis Bero and Krämer, Nicole and Ziegler, Jürgen}, biburl = {https://www.bibsonomy.org/bibtex/253eae0cf029aa7068c4838931205c9ea/brusilovsky}, booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization}, day = 28, description = {Dynamic Ridge Plot Sliders: Supporting Users' Understanding of the Item Space Structure and Feature Dependencies in Interactive Recommender Systems | Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3631700.3664872}, interhash = {fac0924c336852d33c84719b097c5838}, intrahash = {53eae0cf029aa7068c4838931205c9ea}, isbn = {9798400704666}, keywords = {interactive-recommender umap2024 user-control}, location = {Cagliari, Italy}, month = {6}, pages = {106–113}, publisher = {Association for Computing Machinery}, series = {UMAP Adjunct '24}, timestamp = {2024-07-01T17:25:49.000+0200}, title = {Dynamic Ridge Plot Sliders: Supporting Users' Understanding of the Item Space Structure and Feature Dependencies in Interactive Recommender Systems}, url = {https://doi.org/10.1145/3631700.3664872}, year = 2024 }