ABSTRACT Social recommender is an active research area. Most previous social recommenders adopt existing social networks to augment recommendations which are based on user preferences. In this contribution, we propose to simultaneously infer the social influence network and the user preferences in a matrix factorization framework. Furthermore, we assume that the influence strength is dependent on the social roles of users. We present an incremental clustering algorithm to detect dynamic social roles. Comprehensive experiments on real data sets demonstrate the efficiency and effectiveness of our model to generate precise recommendations. INDEX TERMS Social role, recommender system, social recommendation, matrix factorization.
Based on the assumption that the influence strength is dependent on the social roles of users, the authors of REF propose an incremental clustering algorithm to detect dynamic social roles.
Recommendation With Social Roles
IEEE Access
IEEE Access
Computer Science