A hybrid recommendation method integrating the social trust network and local social influence of users

Abstract

Recommendation system plays an indispensable role in helping users make decisions in different application scenarios. The issue about how to improve the accuracy of a recommendation system has gained widespread concern in both academic and industry fields. To solve this problem, many models have been proposed, but most of them usually focus on a single perspective. Different from the existing work, we propose a hybrid recommendation method based on the users’ social trust network in this study. The proposed method has several advantages over conventional recommendation solutions. First, it offers a reliable two-step way of determining reference users by employing direct trust between users in the social trust network and setting a similarity threshold. Second, it improves the traditional collaborative filtering (CF) method based on a Pearson Correlation Coefficient (PCC) to reduce extreme values in prediction. Third, it introduces a personalized local social influence (LSI) factor into the improved CF method to further enhance the prediction accuracy. Seventy-one groups of random experiments based on the real dataset Epinions in social networks verify the proposed method. The experimental results demonstrate its feasibility, effectiveness, and accuracy in improving recommendation performance.