Collaborative Filtering Algorithm Based on Weighted Synthesis Method of Eigenvalues Factors

Abstract

The traditional collaborative filtering algorithm has brought the fundamental change to the present net-business intelligent recommendation, but it is more and more high that the user to the recommendation precision and the personalization request, this algorithm to the user-project non-rational judgment flaw and the sparsity and so on question, seriously affects the recommendation precision and the personalization. Based on these problems, in this paper, a collaborative filtering recommendation algorithm based on factor weighted synthesis is proposed, which calculates the similarity between the user-project's scoring, the dump, the eigenvalues, and so on, and the Matrix is based on the division of the weight vector with the characteristic factor value of the project and the user. Secondly, the recommendation set is obtained by the factor weighted synthesis method, so as to achieve accurate, personalized and highly effective recommendation. The experimental results show that the algorithm can effectively improve the accuracy of similarity calculation to solve the problem of data sparsity and grading, and improve the precision, individuation and execution efficiency of the proposed method.