Collaborative filtering using orthogonal nonnegative matrix tri-factorization |
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Authors: | Gang Chen Fei WangAuthor VitaeChangshui ZhangAuthor Vitae |
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Institution: | State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China |
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Abstract: | Collaborative filtering aims at predicting a test user’s ratings for new items by integrating other like-minded users’ rating information. The key assumption is that users sharing the same ratings on past items tend to agree on new items. Traditional collaborative filtering methods can mainly be divided into two classes: memory-based and model-based. The memory-based approaches generally suffer from two fundamental problems: sparsity and scalability, and the model-based approaches usually cost too much on establishing a model and have many parameters to be tuned. |
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Keywords: | Collaborative filtering Orthogonal nonnegative matrix tri-factorization Co-clustering Fusion |
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