Social context-aware trust prediction in social networks

Online social networks have been widely used for a large number of activities in recent years. Utilizing social network information to infer or predict trust among people to recommend services from trustworthy providers have drawn growing attention, especially in online environments. Conventional trust inference approaches predict trust between people along paths connecting them in social networks. However, most of the state-of-the-art trust prediction approaches do not consider the contextual information that influences trust and trust evaluation. In this paper, we first analyze the personal properties and interpersonal properties which impact trust transference between contexts. Then, a new trust transference method is proposed to predict the trust in a target context from that in different but relevant contexts. Next, a social context-aware trust prediction model based on matrix factorization is proposed to predict trust in various situations regardless of whether there is a path from a source participant to a target participant. To the best of our knowledge, this is the first context-aware trust prediction model in social networks in the literature. The experimental analysis illustrates that the proposed model can mitigate the sparsity situation in social networks and generate more reasonable trust results than the most recent state-of-the-art context-aware trust inference approach.