Research on collaborative filtering recommendation algorithm with improved K-means clustering
Xudong Wei, Zhaofei Li, Li Ji · 2023
In response to the issues of data sparsity, limited scalability, and low recommendation accuracy faced by traditional collaborative filtering algorithms used for personalized user recommendations in the face of increasing data volumes, an optimized collaborative filtering algorithm is proposed that addresses these problems by improving the initial clustering center and incorporating Weighted Slop One filling. This algorithm first uses user similarity as the basis for clustering, and utilizes the Weighted Slope One algorithm to predict the user-rating matrix. Then, the generated matrix is clustered using the AP-MMD-K-means algorithm to obtain a set of similar users. Finally, nearest neighbor search is performed on the target user within their corresponding similar set to generate the recommendation results. Comparative experimental results show that this algorithm improved both recommendation performance and accuracy compared to traditional recommendation algorithms. This algorithm effectively alleviates the problem of data sparsity and improves scalability, significantly improving recommendation accuracy.