Combine Trust and Interest Similarity for Enhanced-Quality Recommendations
Peng Fei Yu · 2018
Collaborative filtering based methods have a low performance in the context of social recommendation due to the data sparsity issue and not considering the social network information that can be exploited to improve the performance. Trust-based methods attempt to reduce the data sparsity by utilizing the social network information. In this paper, we propose a hybrid personal trust model which adaptively combines the rating-based trust model and explicit trust metric to resolve the drawback caused by insufficient past rating records. The proposed method takes user-item matrix and user interaction information as inputs, and calculates the degree of direct trust to produce an initiative trust matrix. Then, following the predefined trust propagation rules, the algorithm infers the degree of indirect trust between users and transforms the initiative trust matrix to a denser trust matrix. The denser trust matrix and rating matrix are collectively used to find the k nearest neighbors for the target user. We present our empirical experiments in a real sparse data set MovieLens, the experimental results shows our algorithm can increase the data density and achieve lower MAE, that is to say, the proposed approach can efficiently improve recommendation quality.