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.
Highlights
With the rapid development of network technology, various applications and corresponding online services become available to people in open and distributed environments such as e-commerce, social networks, cloud computing, Internet of Things, and so on
An improvement in the traditional collaborative filtering (CF) method based on Pearson Correlation Coefficient (PCC) is presented to reduce extreme values of prediction, which is integrated into the proposed method to increase the accuracy and effectiveness of prediction
Experiment 2 is to make comparisons between the traditional CF method and the improved CF method employing PCC based on the social trust network
Summary
With the rapid development of network technology, various applications and corresponding online services become available to people in open and distributed environments such as e-commerce, social networks, cloud computing, Internet of Things, and so on. An enormous number of products and services make it difficult for users to search for what they desire most [1,2,3]. Recommendation systems have gained a rapid growth of attention as a tool to solve the problem of information overload for a wide range of applications in various fields [2,5,6]. It is of significance for recommendation systems to provide users with appropriate and personalized recommendations about services, products, and information. The CF-based recommendation methods can be further subdivided into two basic categories: memory-based and model-based [1,15]
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