Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 402-406.doi: 10.11896/j.issn.1002-137X.2017.6A.091

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Collective Matrix Factorization Algorithm Based on Bias Amendment

LI Ming, YUE Bin and DAI Yong-ping   

  • Online:2017-12-01 Published:2018-12-01

Abstract: Although matrix factorization model has become the major method in the collaborative filtering,it ignores the combined influence of the user bias and latentitems characteristics on recommendation quality.Therefore,this research proposed a collective matrix factorization algorithm,which factorizes items rating matrix and items co-occurrence matrix to amend user bias based on matrix factorization model.The experimental results from different benchmark datasets prove the rationality of the combined factorization algorithm,and indicate greater improvement in the ranking-based metrics in comparison with the traditional matrix factorization model.

Key words: Collaborative filtering,Matrix factorization,Bias amendment,Implicit feedback

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