Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
IEEE Transactions on Knowledge and Data Engineering
PageSim: a novel link-based measure of web page aimilarity
Proceedings of the 15th international conference on World Wide Web
Similarity Measure and Instance Selection for Collaborative Filtering
International Journal of Electronic Commerce
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Simrank++: query rewriting through link analysis of the click graph
Proceedings of the VLDB Endowment
Calculating Similarity Efficiently in a Small World
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
MatchSim: a novel neighbor-based similarity measure with maximum neighborhood matching
Proceedings of the 18th ACM conference on Information and knowledge management
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Collaborative filtering is a widely used recommending method. But its sparsity problem often happens and makes it defeat when rate data is too few to compute the similarity of users. Sparsity problem also could result into error recommendation. In this paper, the notion of SimRank is used to overcome the problem. Especially, a novel weighted SimRank for rate bi-partite graph, SimRate, is proposed to compute similarity between users and to determine the neighbor users. SimRate still work well for very sparse rate data. The experiments show that SimRate has advantage over state-of-the-art method.