Author ORCID Identifier
Document Type
Article
Publication Date
8-27-2017
Publication Title
CEUR Workshop Proceedings
Volume
17
Abstract
The beyond-relevance objectives of recommender system are drawing more and more aention. For example, a diversity-enhanced interface has been shown to positively associate with overall levels of user satisfaction. However, lile is known about how a diversityenhanced interface can help users to accomplish various real-world tasks. In this paper, we present a visual diversity-enhanced interface that presents recommendations in a two-dimensional scaer plot. Our goal was to design a recommender system interface to explore the dierent relevance prospects of recommended items in parallel and to stress their diversity. A within-subject user study with real-life tasks was conducted to compare our visual interface to a standard ranked list interface. Our user study results show that the visual interface signiantly reduced exploration orts required for explored tasks. Also, the users’ subjective evaluation shows signcant improvement on many user-centric metrics. We show that the users explored a diverse set of recommended items while experiencing an improvement in overall user satisfaction.
Recommended Citation
Tsai, Chun-Hua and Brusilovsky, Peter, "Enhancing Recommendation Diversity Through a Dual Recommendation Interface" (2017). Information Systems and Quantitative Analysis Faculty Publications. 128.
https://digitalcommons.unomaha.edu/isqafacpub/128
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
This is an open access publication that is licensed under the Creative Commons Attribution and is available at https://ceur-ws.org/Vol-1884/paper2.pdf or https://ceur-ws.org/Vol-1884/