融合用户模糊聚类和相似度的加权Slope One优化

Weighted Slope One Optimization Combining User Fuzzy Clustering and Similarity

在数据集稀疏的情况下传统的Slope One算法推荐效果差、精确度低,并且该算法对所有用户一视同仁,没有考虑用户间相似性和差异性的情况;同时,随着数据量越来越大,实时性也逐渐变差。针对以上问题,进行加权Slope One算法优化的研究。首先,利用模糊聚类技术将不同类型用户进行分类,减少最近邻搜索范围,降低计算复杂度;然后,对加权Slope One计算公式进行改进,在计算中引入皮尔逊相关系数加以限定;最后,在每个类簇中利用改进的加权Slope One算法进行用户评分预测,进而产生推荐集。实验表明,本文算法有效提高了推荐精确度,增强了推荐实时性。

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.