Matrix Factorization in Social Group Recommender Systems

Traditionally, Group Recommender Systems (GRS) apply an aggregation approach, which computes a group rating for each item by estimating unknown individual ratings, for which has been demonstrated that matrix factorization (MF) models are superior to classic nearest-neighbor techniques in individual recommender systems. Moreover, when people are in a group making a choice from alternatives, they tend to change their opinions accordingly to the social influence exerted by others´ group members. Sociological analyses suggest that some social factors express social influence in a group, such as, cohesion, social similarity and social centrality. In this work, we combine a MF model to estimate unknown ratings with a social network analysis (SNA) to evidence possible social influence. Firstly, we present an analysis of the relevance of social factors detected in relation with the members´ opinions and, then, we describe the results obtained when comparing the proposed technique with the classic group recommender technique.