Research on Collaborative Filtering Algorithm Based on Matrix Factorization
Aiming at the problems that the similarity calculation method in collaborative filtering algorithm only considers a single score data,resulting in unsatisfactory recommendation effect,incomplete recommendation results and low efficiency under the condition of sparse data,an improved collaborative filtering recommendation method is proposed,which constructs user charac-terization vector to calculate user similarity through matrix decomposition.Firstly,the user representation matrix and item represen-tation matrix are constructed.Secondly,the loss function is set to make the inner product of user characterization vector and item characterization vector fit the scoring data.Finally,the similarity calculated by user characterization vector and traditional similarity are fused with specific weight.Experiments on MovieLens dataset show that the improved algorithm improves the absolute average er-ror MAE and improves the accuracy of recommendation in the case of sparse data.
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