Study on dual K-means algorithm in collaborative filtering system based on web log

Dongmeng Guo, Yun Liu, Jian Li, Xiong Fei, Yixiang Zhu
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Abstract

A collaborative filtering algorithm is designed and improved in this paper, which can optimize and improve the sparsity and extention of data in systems. We can Use the user's implicit information in the web log to cluster analysis on user set and by using the dual K-means algorithm, and we can use the K-means algorithm in each clustering set to further improve the effectiveness of the recommendation of the target users. By contrasting the experimental results, this method compared to the performance of other collaborative filtering algorithms have better performance in those sites browsed by large amount of users.
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基于web日志的协同过滤系统中的双k均值算法研究
本文设计并改进了一种协同过滤算法,该算法可以优化和提高系统中数据的稀疏性和可扩展性。我们可以利用web日志中的用户隐式信息对用户集进行聚类分析,并使用双K-means算法,在每个聚类集中使用K-means算法,进一步提高目标用户推荐的有效性。通过实验结果对比,该方法相对于其他协同过滤算法的性能,在用户访问量较大的站点具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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