Probability matrix decomposition based collaborative filtering recommendation algorithm

Yili Tan, Huijuan Zhao, Yourong Wang, Min Qiu


With the development of society, mass information has extensively appeared on the Internet. The information nearly includes all the content we needed. But information overload makes people unable to find the information they needed correctly. Collaborative filtering recommendation algorithm can recommend projects for users according to their demands. But traditional recommendation algorithm which has defects such as data sparsity needs to be improved. This study analyzed collaborative filtering recommendation algorithm, put forward an improved collaborative filtering recommendation algorithm based on probability matrix decomposition, and tested the feasibility of the algorithm. The test results demonstrated that the algorithm had a higher accuracy compared to the traditional algorithm, and its mean absolute error and root-mean-square error were significantly smaller than those of the traditional algorithm. Therefore it can be applied in the daily life of people.

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