Abstract: Recommendation system is an effective way to solve the problem of information overload. It is difficult for the traditional recommendation system to select items that meet the user’s personalized preferences from mass data, and the recommendation quality is disappointing. By optimizing the traditional collaborative filtering recommendation algorithm, this paper proposes a matrix factorization algorithm (CLMF) for collaborative regression model in view of data sparsity and other problems. The method uses machine learning algorithm to identify the in-depth characteristics of content information, which increases the information capacity of original data. Constructs auxiliary feature matrix, maximizes the role of feature labels through feature matrix fusion, and combines data labels, semantic information and scoring matrix to get the algorithm framework. The experimental results on the real dataset show that the new recommendation algorithm can effectively solve the problem of missing eigenvalues, improve the data sparsity, significantly enhance the scalability of the algorithm and the coverage.

Key words: recommendation system, collaborative filtering, machine learning, matrix factorization, sparsity, coverage