Enhancing recommendation diversity through a dual recommendation interface

Chun Hua Tsai, Peter Brusilovsky

Research output: Contribution to journalConference article

2 Scopus citations

Abstract

The beyond-relevance objectives of recommender system are drawing more and more attention. For example, a diversity-enhanced interface has been shown to positively associate with overall levels of user satisfaction. However, little is known about how a diversity-enhanced 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 scatter plot. Our goal was to design a recommender system interface to explore the different 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 significantly reduced exploration efforts required for explored tasks. Also, the users' subjective evaluation shows significant 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.

Original languageEnglish (US)
Pages (from-to)10-15
Number of pages6
JournalCEUR Workshop Proceedings
Volume1884
StatePublished - Jan 1 2017
Event4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2017 - Como, Italy
Duration: Aug 27 2017 → …

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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