A Dynamic Local–Global Trust-aware Recommendation approach

Abstract

Abstract Trust-aware recommender systems have attracted much attention in recent years because of the popularity of social networks. Some researchers have proposed local trust models to measure trust between two users based on their interactions. However, in most cases, there are few (if any) direct interactions between two users. In such cases, it is useful to associate a user with a global reputation value that reflects the experience of the whole community with that user. In this paper, we propose a Dynamic Local–Global Trust-aware Recommendation (DLGTR) approach, which uses a new hybrid model of local and global trust. In this hybrid model, the relative importance of local trust versus global trust is dynamically determined based on the reliability of information from the two trust models. Moreover, DLGTR uses an incremental method to efficiently update a trust network as new data become available. This allows DLGTR to constantly improve the recommendation quality without loss of efficiency. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art recommendation approaches, especially in terms of prediction accuracy and computational time.