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Reconstructing Trust Matrix to Improve Prediction Accuracy and Solve Cold User Problem in Recommender Systems

Recommender systems(RS) are a type of solution to the information overload problem suffered by users of websites that allow the rating of certain items. Collaborative filtering(CF) is one of the most widely used methods in personalized RS. The most critical part of collaborative filtering is to compute similarities among users using a user-item rating matrix based on which recommendations can be generated. However, CF suffers from several inherent issues, such as data sparsity and cold start, which affect the quality of recommendation seriously. To address these problems, we propose a reconstructing trust matrix measure in this paper, which combines user similarity and weighted trust propagation. Specifically, we first remove the trust relationship of those users whose similarity falls below a certain threshold. We then add the users that are not in the trust matrix into it when the similarity between them exceeds a certain threshold. Finally, weighted trust propagation is considered, aiming to distinguish trusted neighbors in a shorter distance with those in a longer distance and incorporate more trusted neighbors, especially useful for cold users. Experimental results on two real-world data sets show that our method achieves superior accuracy and it can solve cold user problem as well.