%0 Conference Paper %1 Tsai:2019:EVE:3320435.3320465 %A Tsai, Chun-Hua %A Brusilovsky, Peter %B Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization %C New York, NY, USA %D 2019 %I ACM %K explanation information-visualization recommender similarity %P 22--30 %R 10.1145/3320435.3320465 %T Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance %U http://doi.acm.org/10.1145/3320435.3320465 %X 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. %@ 978-1-4503-6021-0

@inproceedings{Tsai:2019:EVE: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.}, acmid = {3320465}, added-at = {2019-06-11T21:56:41.000+0200}, address = {New York, NY, USA}, author = {Tsai, Chun-Hua and Brusilovsky, Peter}, biburl = {https://www.bibsonomy.org/bibtex/2e9f22e80ca633d93890b0525d686288a/komadagar}, booktitle = {Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization}, description = {Evaluating Visual Explanations for Similarity-Based Recommendations}, doi = {10.1145/3320435.3320465}, interhash = {ffbe7d9faef1d5d3723fa90bd9962096}, intrahash = {e9f22e80ca633d93890b0525d686288a}, isbn = {978-1-4503-6021-0}, keywords = {explanation information-visualization recommender similarity}, location = {Larnaca, Cyprus}, numpages = {9}, pages = {22--30}, publisher = {ACM}, series = {UMAP '19}, timestamp = {2019-06-11T21:56:41.000+0200}, title = {Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance}, url = {http://doi.acm.org/10.1145/3320435.3320465}, year = 2019 }