Collaborative Filtering in Personalized Recommendation Based on Users Pattern Subspace Clustering

Collaborative filtering technology has been successfully used in personalized recommendation systems. With the development of E-commerce, as well as the increase in the number of users and items, the users score data sparsity and the dimension disaster problems have been caused which leads to sharp decline in the quality of their recommend. A calculation of pattern similarity was proposed based on the users pattern similarity to direct at the sparsity and dimension disadvantage of high-dimensional data. Clustering were produced by subspace clustering algorithm based on users pattern similarity, and collaborative filtering algorithm was improved by calculating of model similarity which brings recommendation to users. The experimental result shows that algorithm increase the response speed of the system, at the mean time the recommendation quality has been improved a lot.