期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2013
卷号：6
期号：2
出版社：SERSC
摘要：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 computa-tional 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 accu-rately with low computational complexity
