Improving Recommendation based on Implicit Trust Relationships from Tags
نویسنده
چکیده
In this paper, we proposed an implicit trust relationship extraction approach to alleviate the sparsity problem in recommender systems. The recommender system cannot generate relevant items when a user-item matrix is sparse. It is a serious weakness of collaborative filtering based recommender systems. In social tagging system, tagging information is useful data source for recommendation. We investigate eliciting implicit trust relationships from the tagging information. The relationships are derived by Kullback-Leibler divergence of users’ tagged items and tags. The experimental results show that proposed approach provides relevant items precisely and performs well in
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تاریخ انتشار 2012