Global Rating Prediction Mechanism for Trust-Aware Recommender System using K-Shell Decomposition

The trust-aware recommender system (TARS) suggests the worthwhile information to the users on the basis of trust. Existing models of TARS use personalized rating prediction mechanisms, which can provide personalized services to each user, but they are computational very expensive. We therefore propose an efficient global rating prediction mechanism for TARS: we use the k-shell decomposition to find the most influential nodes in the trust network, and use the recommendations given by these nodes to predict global ratings on items. The experimental results verify that our proposed method can predict ratings accurately with low computational complexity.