Cloud pattern collaborative filtering recommender algorithm using user behavior correlation clustering
Wan Nian-hong · Journal of Computer Applications · 2011
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.