A unified framework of trust prediction based on message passing

Abstract

Trust prediction has been a vital part in trust-aware recommender systems. There are two approaches to predict trust between users: an explicit-information approach and an implicit-information approach. The explicit-information approach uses the explicit trust information observed from a trustor-trustee pair, whereas the implicit-information approach uses the implicit-information inferred from interaction features between a candidate trustor-trustee pair. The existing researches on trust prediction have been mainly focusing on one kind of information rather than combining the two kinds collectively. The task of trust prediction, however, could be improved when all kinds of information available from user pairs are integrated. Thus, we develop a new framework, including trust propagation mechanisms based on trust-message passing which takes advantage of the two kinds of information. In the developed framework, we build various probability-based trust prediction models according to the way of integrating the trust propagation mechanisms. Using real-world data, we empirically determine a weight vector for the integration of the trust propagation mechanisms and select the best trust prediction model. We demonstrate the benefit of the proposed trust propagation mechanisms by showing that the proposed method, ITD, outperforms existing methods, ABIT_L and MoleTrust, by 12.5 and 29.1%, respectively, in accuracy.