Article ID Journal Published Year Pages File Type
8917948 Online Social Networks and Media 2018 11 Pages PDF
Abstract
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.
Related Topics
Physical Sciences and Engineering Computer Science Computer Networks and Communications
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