Collaborative filtering recommendation algorithm based on one-jump trust model - Xing-mao WANG; Xing-ming ZHANG; Jiang-xing WU (Tongxin xuebao)
A collaborative filtering recommendation algorithm based on the trust network of social brings two problems that the choice of complex paths between nodes and the weak transfering of trust.Toward to these two problems,a one-jump trust model based on items was put forward,the model calculated the trust between users and items,defined the consumer’s trust attribute vector of social and calculated the direct and indirect distance one-jump by items,and then calculated the trust between users.A collaborative filtering algorithm(OneJ-TCF) is degined based on the model,moreover analysed and reorganized the relation between trust and similarity.The experiments show that this algorithm improves the degree of accuracy(reducing about 0.02 MAE),and saves about 50% training time at the same time.