Improving User Experience for Recommender System using Diversity

Abstract

Diversity is an important indicator for improving user experience in recommender sys-tems. Previous research indicate that people prefer diverse recommended item lists. However, few studies have experimented with online user experience of recommender systems owing to lack of clarity regarding the effects of diversity of recommender systems on user experience. This paper reports the online experience of diversity of web service recommender systems. We analyzed the recommender system without diversity for user activity in web services. As a result, the second half of the recom-mended list is underwhelming. We have constructed a diverse recommender system by decreasing user features, and have compared our system to the existing system for user activity in web services. Consequently, our system has succeeded in improv-ing the weekly retention and active rates. Therefore, the number of clicks on the recommended list have increased.