A Hybrid Approach for Group Profiling in Recommender Systems

Abstract

Recommendation is a significant paradigm for information exploring, which focuses on the recovery of items of potential interest to users. Some activities tend to be social rather than individual, which puts forward the need to offer recommendations to groups of users. Group recommender systems present a whole set of new challenges within the field of recommender systems. In this paper, we present a hybrid approach based on group profiling for homogeneous and non-homogenous groups containing a few distant individual profiles among their members. This approach combines three familiar individual recommendation approaches: collaborative filtering, content-based filtering and demographic information. This hybrid approach allows the detection of those implicit similarities in the user rating profile, so as to include members with divergent profiles. We also describe the promising results obtained when evaluating the approach proposed in the movie and music domain.