Collaborative Filtering Algorithm Based on MeanShift Clustering
In this era of electricity and big data, the personalized recommendation is mainly used in e-commerce sites. To solve the traditional collaborative filtering algorithm existing the new user, new project, scalability and sparsity problems, proposes a collaborative filtering algorithm based on MeanShift clustering, Semantic similarity between items and Singular Value Decomposition. To deal with the disadvantages that the traditional collaborative filtering recommendation system facing. The experimental results show that, compared with the traditional recommendation algorithm, the improved algorithm has higher accuracy.
Recommendation SystemCollaborative Filtering AlgorithmClusteringSingular Value Decomposition
NETL
NSTL
万方数据
维普
