Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance
Author ORCID Identifier
Document Type
Conference Proceeding
Publication Date
6-7-2019
Publication Title
UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization
Volume
June 2019
First Page
22
Last Page
30
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.
Recommended Citation
Tsai, Chun-Hua and Brusilovsky, Peter, "Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance" (2019). Information Systems and Quantitative Analysis Faculty Publications. 144.
https://digitalcommons.unomaha.edu/isqafacpub/144
Comments
© {Authors | ACM} {2019}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in {UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization}, https://doi.org/10.1145/3320435.3320465