Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A graph model for E-commerce recommender systems
Journal of the American Society for Information Science and Technology
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Complex-network theoretic clustering for identifying groups of similar listeners in p2p systems
Proceedings of the 2007 ACM conference on Recommender systems
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Recently, network based recommendation algorithms have demonstrated much better performance than the standard collaborative filtering method, and most of which have been focused on the unweighted cases even in a multigraded rating system. However, these modifications from multigraded rating data to binary data may lose information, thus hinder the expressing of user's preference and finally misleading the recommendation systems. In this paper, we propose to use weighted bipartite user-object networks to model the recommender systems. The weight of the edge is directly the rate that a user giving on an object. We use a benchmark dataset, i.e., Moivelens dataset, to test the performance. The results show that weighted theme has higher recommendation accuracy than its unweighted counterpart.