Harnessing heterogeneous social networks for better recommendations: A grey relational analysis approach

Abstract Most of the extant studies in social recommender system are based on explicit social relationships, while the potential of implicit relationships in the heterogeneous social networks remains largely unexplored. This study proposes a new approach to designing a recommender system by employing grey relational analysis on the heterogeneous social networks. It starts with the establishment of heterogeneous social networks through the user-item bipartite graph, user social network graph and user-attribute bipartite graph; and then uses grey relational analysis to identify implicit social relationships, which are then incorporated into the matrix factorization model. Five experiments were conducted to test the performance of our approach against four state-of-the-art baseline methods. The results show that compared with the baseline methods, our approach can effectively alleviate the sparsity problem, because the heterogeneous social network provides richer information. In addition, the grey relational analysis method has the advantage of low requirements for data size and efficiently relieves the cold start problem. Furthermore, our approach saves processing time, thus increases recommendation efficiency. Overall, the proposed approach can effectively improve the accuracy of rating prediction in social recommendations and provide accurate and efficient recommendation service for users.