Social Trust-Aware Recommendation System: A T-Index Approach

Collaborative Filtering based on similarity suffers from a variety of problems such as sparsity and scalability. In this paper, we propose an ontological model of trust between users on a social network to address the limitations of similarity measure in Collaborative Filtering algorithms. For enhancing the constructed network of users based on trust, we introduce an estimate of a user’s trustworthiness called T-index to identify and select neighbors in an effective manner. We employ T-index to store raters of an item in a so-called TopTrustee list which provides information about users who might not be accessible within a predefined maximum path length. An empirical evaluation shows that our solution improves both prediction accuracy and coverage of recommendations collected along few edges that connect users on a social network by exploiting T-index. We also analyze effect of T-index on structure of trust network to justify the results.