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.
Huang and Lin REF assumed that the accuracy of recommendation depends on the social roles of users, and an incremental clustering algorithm for detecting dynamic social roles was proposed.
Recommendation With Social Roles
IEEE Access
IEEE Access
Computer Science