Friend recommendation of microblog in classification framework: Using multiple social behavior features

Abstract

In recent years, microblog has been experiencing an explosive growth, which brings much inconvenience to users to build a healthy social circle in this chaos online world. Friend recommendation can automatically recommend potential friends, filter out the useless information, and facilitate the healthy development of social network. A novel friend recommendation approach is proposed in this paper. First, three kinds of social behavior features, i.e., social rating feature, social content features and social relation features, are extracted to represent the relationship of each user pair in the large-scale microblog data. Based on these features, a binary classifier is trained to determine whether the second user in each pair should be recommended to the first one. In this way, the original recommendation problem is transformed to a binary classification problem so that the sparseness problem of collaborative filtering method can be solved properly. Experiments shows that our approach improves the performance of friend recommendation compared with the traditional collaborative filtering methods.