Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles

Recommender systems (RS) have been widely employed to suggest personalized online information to simplify user's information discovery process. With the popularity of online social networks, analysis and mining of social factors and social circles have been utilized to support more effective recommendations, but have not been fully investigated. In this paper, the authors propose a novel recommendation model with the consideration of more comprehensive social factors and topics that user is explicitly and implicitly interested in. Concretely, to further enhance recommendation accuracy, four social factors, individual preference, interpersonal trust influence, interpersonal interest similarity and interpersonal closeness degree, are simultaneously injected into our recommendation model based on probabilistic matrix factorization. Meanwhile, the authors explore several new methods to measure these social factors. Moreover, the authors infer explicit and implicit social circles to enhance the performance of recommendation diversity. Finally, the authors conduct a series of experiments on publicly available data. Experimental results show the proposed model achieves significantly improved performance (accuracy and diversity) over the existing models in which social information have not been fully considered.