Entertainment recommender systems for group of users

Recommender systems are used to recommend potentially interesting items to users in different domains. Nowadays, there is a wide range of domains in which there is a need to offer recommendations to group of users instead of individual users. As a consequence, there is also a need to address the preferences of individual members of a group of users so as to provide suggestions for groups as a whole. Group recommender systems present a whole set of new challenges within the field of recommender systems. In this article, we present two expert recommender systems that suggest entertainment to groups of users. These systems, jMusicGroupRecommender and jMoviesGroupRecommender, suggest music and movies and utilize different methods for the generation of group recommendations: merging recommendations made for individuals, aggregation of individuals’ ratings, and construction of group preference models. We also describe the results obtained when comparing different group recommendation techniques in both domains.

► We present two expert recommender systems that suggest entertainment to groups of users in movie and music domains. ► The systems were built based on a framework we developed, which provides techniques to generate group recommendations focusing on the three approaches most widely used in this area. ► We describe the results obtained when comparing different group recommendation techniques in both domains.