Inproceedings,
Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance
Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, page 22--30. New York, NY, USA, ACM, (2019)
DOI: 10.1145/3320435.3320465
Abstract
Recommender system helps users to reduce information overload. In recent years, enhancing explainability in recommender systems has drawn more and more attention in the field of Human-Computer Interaction (HCI). However, it is not clear whether a user-preferred explanation interface can maintain the same level of performance while the users are exploring or comparing the recommendations. In this paper, we introduced a participatory process of designing explanation interfaces with multiple explanatory goals for three similarity-based recommendation models. We investigate the relations of user perception and performance with two user studies. In the first study (N=15), we conducted card-sorting and semi-interview to identify the user preferred interfaces. In the second study (N=18), we carry out a performance-focused evaluation of six explanation interfaces. The result suggests that the user-preferred interface may not guarantee the same level of performance.
