Groups Identification and Individual Recommendations in Group Recommendation Algorithms.

Abstract

Recommender systems usually deal with preferences previously expressed by users, in order to predict new ratings and recommend items. To support recommendation in social activities, group recommender systems were developed. Group recommender systems usually consider predened/a priori known groups and just a few existing approaches are able to automatically identify groups. When groups are not already formed, another key aspect of group recommendation is related to groups identication. In this paper a novel algorithm able to identify groups of users and produce recommendations for each group is presented. The algorithm uses individual recommendations and a classic clustering algorithm to identify and model groups. Experimental results show how this approach substantially improves the quality of group recommendations with respect to the state-of-the-art.