A Personalized Recommendation Algorithm Based on Sparse Matrix Partition

In this paper,we propose a personalized recommendation method based on sparse matrix partition.In our ap-proach,the user-item rating matrix can be partitioned into low-dimensional dense matrices using classification methods or clustering methods.The recommendations are generated based on low-dimensional matrices.Moreover,compared traditional collaborative filtering method,the experimental results show the effectiveness and efficiency of our approach.