关键词: 个性化推荐, 社会网络, 权重, 复杂网络, CPM聚类

Abstract: Personal recommendation is a crucial implementation to solve the problem of information overloading on the Internet. On the basis of researching personal recommendation skills and corresponding technologies, an application-driven personal recommendation algorithm in multidimensional and weighted social network was proposed. First, this algorithm built multidimensional and weighted social network between users, then applied the complex network clustering method—CPM (Clique Percolation Method) to find neighbor users, finally made recommendation on the grounds of the similarity between users. The experimental results show that the recommendation system of multidimensional network applying this algorithm can achieve higher recall and precision compared to content-based and collaborative filtering recommendation systems, and the quality of personal recommendation has been improved to some extent.

Key words: personal recommendation, social network, weight, complex network, CPM (Clique Percolation Method) clustering