Social affinity-based group recommender system

Abstract

Information collected from the social network is recently used to improve a performance of recommender systems to an individual user or a group. During selecting the items among the group members, the relationships (e.g., position, dependency, and the strength of the social ties) often has an important role than the individual preference in the group. Hence, we propose a novel recommendation method based on social affinity between two users. This recommendation method consists of (i) the similarity calculation between movies based on weighted feature, (ii) the generation of initial affinity network graph, and (iii) the computation of user’s affinity to group based on the graph. Experimental results on synthetic dataset show that our proposed method can discover social affinities efficiently.