Trust-based Top-k Item Recommendation in Social Networks ⋆

Collaborative filtering based methods have a low performance in the context of social recommendation due to the data sparsity issue and not considering the social network information that can be exploited to improve the performance. Trust-based methods attempt to reduce the data sparsity by utilizing the social network information. However, most of these methods are based on the explicit trust statements expressed by users, which are not available in the social networks such as Sina Weibo. In this paper, we present a trust metric to quantitatively measure the recommendation trust between pairs of users by aggregating the implicit trust and trust propagation values. We propose a trust-based latent factor model, which incorporates the pairwise recommendation trust values into the probabilistic model for top-k item recommendation. The experiments on Sina Weibo demonstrate that our method outperforms the collaborative filtering based methods and trust-based methods.