A Robust Collaborative Filtering Recommendation Algorithm Based on Multidimensional Trust Model

Collaborative filtering is one of the widely used technologies in the e-commerce recommender systems. It can predict the interests of a user based on the rating information of many other users. But the traditional collaborative filtering recommendation algorithm has the problems such as lower recommendation precision and weaker robustness. To solve these problems, in this paper we present a robust collaborative filtering recommendation algorithm based on multidimensional trust model. Firstly, according to the rating information of users, a multidimensional trust model is proposed. It measures the credibility of user’s ratings from the following three aspects: the reliability of item recommendation, the rating similarity and the user’s trustworthiness. Secondly, the computational model of trust and the traditional collaborative filtering approach are combined to select the reliable neighbor set and generate recommendation for the target user. Finally, the performances of the novel algorithm with others are compared from both sides of recommendation precision and robustness using MovieLens dataset. Compared with the existing algorithms, the proposed algorithm not only improves the quality of neighbor selection and the recommendation precision, but also has better robustness.