Recommendation algorithm of probabilistic matrix factorization based on directed trust

This paper focuses on improving the performance of recommender systems by the use of social trust information. Probabilistic matrix factorization is a classic algorithm for recommender systems. However, both the rating matrix and trust matrix become sparser, which makes the recommended results inaccurate. We propose a hybrid method based on probabilistic matrix factorization and directed trust. First, we apply the probabilistic matrix factorization approach to break down the trust matrix. Thus, the potential preferences of users, considering trusters and trustees, are obtained. This approach alleviates the problem of the sparsity of the trust matrix. Second, to capture the trust relations among users, we modify undirected trust to directed trust, since a user has different preferences when he is treated as a truster or trustee. Last, the two algorithms are combined to predict ratings. Experiments involving two datasets show that the proposed algorithm is superior to existing benchmark algorithms.

Automated summary

This paper proposes a hybrid method based on probabilistic matrix factorization and directed trust to improve the performance of recommender systems, addressing the sparsity of trust matrix and capturing trust relations among users. Experimental results demonstrate that the proposed algorithm outperforms existing benchmark algorithms.