Evaluating visual explanations for similarity-based recommendations: User perception and performance

Chun Hua Tsai, Peter Brusilovsky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages22-30
Number of pages9
ISBN (Electronic)9781450360210
DOIs
StatePublished - Jun 7 2019
Event27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019 - Larnaca, Cyprus
Duration: Jun 9 2019Jun 12 2019

Publication series

NameACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019
Country/TerritoryCyprus
CityLarnaca
Period6/9/196/12/19

All Science Journal Classification (ASJC) codes

  • Software

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