Affiliations: College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
Correspondence:
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Corresponding author: Jin Gou, College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China. E-mail: [email protected].
Abstract: Collaborative filtering is a popular tool for recommendation systems. However, collaborative filtering technologies often suffer from high time complexity, the cold-start problem, and low coverage. Recent research shows that social networks and trust-aware methods can effectively solve these problems. Therefore, we propose a Trust Domain Expert Collaborative Filtering recommendation system. First, we divide the user item rating matrix into multiple sub-matrices based on the domain attributes of each item. For each sub-matrix, we then use domain experts to construct a user–expert trust matrix. Finally, combined with the target user’s domain of interest, we predict their missing ratings. Experimental results show that this method not only improves the accuracy and recommended coverage of collaborative filtering-based methods, but also reduces the computation time.
