An Efficient Trust Inference Algorithm with Local Weighted Centrality for Social Recommendation

The integration of trust system and recommendation system is a new hot spot in current research. Trust relationship has be exploited in social recommendation, which can effectively solve the problems of low recommendation quality, sparse data and cold start in the traditional recommendation system. Meanwhile, trust inference in social relations is necessary in completing trust information and expanding social recommendation knowledge base. In this work, we propose a new trust inference algorithm LWCTrust to improve the efficiency and accuracy of social recommendation. Firstly, we construct a local weighted centrality (LWC) metric based on the user’s degree centrality and trust information, and propose a new adaptive breadth-first search algorithm. Then, based on the property of path decay, we compare two different trust decay strategies. In addition, considering inconsistencies and conflicts in trust opinion, we apply LWC metric to multi-path aggregation step and present a OWA dynamic aggregation strategy. A number of experiments are conducted on the real social network dataset Advogato, and the results validate the great performance of LWCTrust. Our work is the first to construct an efficient LWC metric using social graph trust information, and we explore the effect of attenuation functions on accuracy in path propagation.