SOM Clustering Collaborative Filtering Algorithm Based on Singular Value Decomposition

Abstract

The application of traditional collaborative filtering algorithm on large-scale commercial websites is very mature. However, the data sparsity and extensibility problems that occur in the algorithm affect the recommendation accuracy of the algorithm. In order to solve this problem, a SOM clustering collaborative filtering algorithm based on singular value decomposition is proposed. Firstly, the original sparse matrix is reduced by the singular value decomposition, and the items are evaluated in the low-dimensional space, the prediction results are filled in the original matrix, which alleviates the problem of data sparseness. Then use SOM to cluster the users, which reduces the range of users searching for neighbors and improves the scalability of the algorithm. The experimental results on MovieLens-100k show that the algorithm can effectively improve the accuracy of the recommendation.