Weighted Slope One Optimization Combining User Fuzzy Clustering and Similarity

Abstract

Abstract: In the case of sparse data sets, the traditional Slope One algorithm has poor recommendation and low accuracy, and the algorithm treats all users equally without considering the similarities and differences between users. At the same time, as the amount of data increases, the real-time performance has gradually deteriorated. In view of the above problems, a weighted Slope One algorithm optimization study is carried out. Firstly, we use fuzzy clustering technology to classify different types of users and reduce the nearest neighbor search range and calculation complexity. Then, we improve the weighted Slope One calculation formula and use the Pearson correlation coefficient to limit the calculation. finally, we use the improved weighted Slope One algorithm to predict user ratings in each cluster, and then generate a recommendation set. Experiments show that the algorithm in this paper effectively improves the accuracy of recommendations and enhances the real-time performance of recommendations.

Key words: collaborative filtering, weighted Slope One algorithm, fuzzy clustering, recommendation algorithm

SHI Peng, YAO Wen-ming, WANG Xiang. Weighted Slope One Optimization Combining User Fuzzy Clustering and Similarity[J]. Computer and Modernization, 2021, 0(01): 70-75.

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