Social trust model for rating prediction in recommender systems: Effects of similarity, centrality, and social ties

Abstract

The success of e-commerce companies is becoming increasingly dependent on product recommender systems which have become powerful tools that personalize the shopping experience for users based on user interests and interactions. Most modern recommender systems concentrate on finding the relevant items for each user based on their interests only, and ignore the social interactions among users. Some recommender systems, rely on the ‘trust’ of users. However in social science, trust, as a human characteristic, is a complex concept with multiple facets which has not been fully explored in recommender systems.In this paper, to model a realistic and accurate recommender system, we address the problem of social trust modeling where trust values are shaped based users characteristics in a social network. We propose a method that can predict rating for personalized recommender systems based on similarity, centrality and social relationships. Compared with traditional collaborative filtering approaches, the advantage of the proposed mechanism is its consideration of social trust values. We use the probabilistic matrix factorization method to predict user rating for products based on user-item rating matrix. Similarity is modeled using a rating-based (i.e., Vector Space Similarity and Pearson Correlation Coefficient) and connection-based similarity measurements. Centrality metrics are quantified using degree, eigen-vector, Katz and PageRank centralities. To validate the proposed trust model, an Epinions dataset is used and the rating prediction scheme is implemented. Comprehensive analysis shows that the proposed trust model based on similarity and centrality metrics provide better rating prediction rather than using binary trust values. Based on the results, we find that the degree centrality is more effective compared to other centralities in rating prediction using the specific dataset. Also trust model based on the connection-based similarity performs better compared to the Vector Space Similarity and Pearson Correlation Coefficient similarities which are rating based. The experimental results on real-world dataset demonstrate the effectiveness of our proposed model in further improving the accuracy of rating prediction in social recommender systems.