Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Probabilistic models of text and images
Probabilistic models of text and images
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Combinational collaborative filtering for personalized community recommendation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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Personalized recommendation techniques play more and more important roles for the explosively increasing of information nowadays. As a most popular recommendation approach, collaborative filtering (CF) obtains great success in practice. To overcome the inherent problems of CF, such as sparsity and scalability, we proposed a semantic based mixture CF in this paper. Our approach decomposes the original vector into semantic component and residual component, and then combines them together to implement recommendation. The semantic component can be extracted by topic model analysis and the residual component can be approximated by top values selected from the original vector respectively. Compared to the traditional CF, the proposed mixture approach has introduced semantic information and reduced dimensions without serious information missing owe to the complement of residual error. Experimental evaluation demonstrates that our approach can indeed provide better recommendations in both accuracy and efficiency.