On Achieving Diversity in Recommender Systems

Abstract

Throughout our digital lives, we are getting recommendations for about almost everything we do, buy or consume. In that way, the field of recommender systems has been evolving vastly to match the increasing user needs accordingly. News, products, ideas and people are only a few of the things that we can be recommended with daily. However, even with the many years of research, several areas still remain unexplored. The focus of this paper revolves around such an area, namely on how to achieve diversity in single-user and group recommendations. Specifically, we decouple diversity from strictly revolving around items, and consider it as an orthogonal dimension that can be incorporated independently at different times in the recommender's workflow. We consider various definitions of diversity, taking into account either data items or users characteristics, and study how to cope with them, depending on whether we opt at diversity-aware single-user or group recommendations.