Research on Collaborative Filtering Recommendation Based on User Fuzzy Clustering
基于用户模糊聚类的协同过滤推荐研究

Traditional collaborative filtering algorithm does not consider the influence caused by the users' information, and the existing issues such as data sparsity, poor scalability and others directly affecte the recommendation quality of recommendation systems. To address these issues, a collaborative filtering algorithm based on user context fuzzy clustering was proposed. First, users are clustered by fuzzy clustering algorithm according to user context, then the user-item rating matrix should be filled through slope one algorithm in advance before the traditional collaborative filtering.This effectively improves the sparsity of user rating data and the real-time performance. The experimental results indicate that the recommendation accuracy of the advanced approach is largely improved.