云模式用户行为关联聚类的协同过滤推荐算法

传统的协同过滤推荐算法基于互联网模式单纯从某个角度研究电子商务推荐问题，推荐质量明显不高。为改善推荐效果，提高推荐系统的伸缩性和实用价值，基于研究云模式的用户行为相似性度量公式、用户行为等级函数、关联规则函数，定义关联聚类方法，改进相应算法，提出一种云模式用户行为关联聚类的协同过滤推荐算法。最后使用MovieLens和阿里巴巴的云测试数据进行局部实验与全局实验，并对各种算法的实验结果进行对比分析。实验结果表明，该算法推荐效果明显优于传统算法，具有较强的伸缩性和较高的实用价值。

Cloud pattern collaborative filtering recommender algorithm using user behavior correlation clustering

The traditional collaborative filtering recommender algorithms based on Internet pattern research merely E-commerce recommender problem from one angle, and their recommender quality is evidently not high. To improve recommender efficiency, and to achieve scalability and utility of recommendation systems, with studying user behavior similarity measure formula, grade function and correlation rule function based on cloud pattern, a correlation clustering method was put forward. To improve the corresponding algorithms, a cloud pattern collaborative filtering recommender algorithm based on user behavior correlation clustering was proposed. Finally, the improved algorithms were validated by local and global experiments using MovieLens and Alibaba cloud testing data. The experimental results show that the recommender efficiency of the proposed algorithm is obviously higher than those of traditional algorithms, and it has stronger scalability and higher utility.