Groups identification and individual recommendations in group recommendation algorithms, Computer

Recommender systems usually deal with preferences previ-ously expressed by users, in order to predict new ratings and recommend items. To support recommendation in so-cial activities, group recommender systems were developed. Group recommender systems usually consider predefined/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 identification. In this paper a novel algorithm able to identify groups of users and produce recommendations for each group is pre-sented. 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