关键词: 好友推荐, 三部图, 标签, 社交网络, 协同推荐

Abstract:  As the exponential growth of the number of users in social networks, how to recommend friends with similar interests from massive users has become one of the research focuses in social recommendation. This paper proposes a hybrid friend recommendation algorithm based on user-item-tag graph and users' personal interests. Firstly, the similarities between users are calculated by mass diffusion method in tripartite graph. Secondly, the relationship between users and tag graph of users is introduced, and the communities in the tag graphs of users are detected for the topics of users' interests. Then the similarities between users are measured by Kullback-Leibler divergence according to their topics distributions. Finally, two kinds of similarities are integrated for user recommendation by the harmonic mean method. The experimental results on Delicious and Last.fm datasets demonstrate that the proposed algorithm can effectively improve the accuracy of Top-N recommendation in terms of precision and recall. At the same time, the experimental results on the academic social network site—SCHOLAT for the scholar recommendation prove that the proposed algorithm improves the recommendation of core network members.

Key words: friend recommendation, tripartite graph, tag, social network, collaborative filtering