Collaborative filtering recommendation based on trust and emotion

With the development of personalized recommendations, information overload has been alleviated. However, the sparsity of the user-item rating matrix and the weak transitivity of trust still affect the recommendation accuracy in complex social network environments. Additionally, collaborative filtering based on users is vulnerable to shilling attacks due to neighbor preference recommendation. With the objective of overcoming these problems, a collaborative filtering recommendation method based on trust and emotion is proposed in this paper. First, we employ a method based on explicit and implicit satisfaction to alleviate the sparsity problems. Second, we establish trust relationships among users using objective and subjective trust. Objective trust is determined by similarity of opinion, including rating similarity and preference similarity. Subjective trust is determined by familiarity among users based on six degrees of separation. Third, based on the trust relationship, a set of trusted neighbors is obtained for a target user. Next, to further exclude malicious users or attackers from the neighbors, the set is screened according to emotional consistency among users, which is mined from implicit user behavior information. Finally, based on the ratings of items by the screened trusted neighbors and the trust relationships among the target user and these neighbors, we can obtain a list of recommendations for the target user. The experimental results show that the proposed method can improve the recommendation accuracy in the case of data sparsity, effectively resist shilling attacks, and achieve higher recommendation accuracy for cold start users compared to other methods.