Research on Collaborative Filtering Recommendation Algorithm Based on Fuzzy Clustering

In e-commerce, it is very important to understand the preferences of users to better personalize services. Therefore, the recommendation system is particularly important, and the traditional collaborative filtering algorithm in the recommendation system cannot better solve the accuracy problem in the recommendation system. Therefore, this paper proposes a collaborative filtering recommendation algorithm based on fuzzy clustering for recommendation system analysis. Firstly, fuzzy clustering is used to cluster items in e-commerce, and indicators are divided according to the requirements of the recommendation system to reduce the recommendation system in the interfering factor. Then, fuzzy clustering optimizes the personalized service recommendation system to form a recommendation system scheme and performs the recommendation system results Comprehensive analysis. MATLAB simulation shows that under certain evaluation criteria, the collaborative filtering recommendation algorithm based on fuzzy clustering has a good effect on the accuracy of the recommendation system of personalized services. The recommendation accuracy of the recommendation system is better than that of the traditional collaborative filtering algorithm.