Probability Matrix Decomposition Based Collaborative Filtering Recommendation Algorithm.

With the development of the society, the increased amount of information has extensively appeared on the Internet. It includes almost all the content we need. But information overload makes people unable to correctly find the information they need. Collaborative filtering recommendation algorithm can recommend items for users according to their demands. But traditional recommendation algorithm which has defects such as data sparsity needs to be improved. In this study, the collaborative filtering recommendation algorithm was analyzed, an improved collaborative filtering recommendation algorithm based on the probability matrix decomposition was put forward, and the feasibility of the algorithm was verified. Moreover the traditional algorithms including user based collaborative filtering algorithm, item based collaborative filtering algorithm, singular value decomposition based collaborative filtering algorithm and basic matrix based collaborative filtering algorithm were tested. The test results demonstrated that the proposed algorithm had a higher accuracy compared to the traditional algorithms, and its mean absolute error and root-mean-square error were significantly smaller than those of the traditional algorithms. Therefore it can be applied in the daily life.