A Novel Framework for Top-N Recommendation Based on Non-negative Matrix Tri-Factorization
Clustering techniques have been proved effective to deal with the sparsity and scalability problems in collaborative filtering recommender systems. They aim to identify groups of users having similar preferences or items sharing similar topics. In this study, we propose an integrated recommendation framework based on matrix factorization. Firstly, users and items are clustered into multiple groups and a pair of strongly related user group and item group forms a submatrix. Then some traditional collaborative filtering technique is executed in every submatrix. The final rating predictions are generated by aggregating results from all the submatrices and the items are recommended with a Top-N strategy. Experimental results show that the proposed framework significantly improves the recommendation accuracy of several state-of-the-art collaborative filtering methods, while retains the advantage of good scalability.
KeywordsMatrix tri-factorization Collaborative filtering Co-clustering Personalized recommendation
The work was supported by the General Program of the National Science Foundation of China (Grant No. 71471127, 71371135).
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