Social Friend Recommendation Based on Network Correlation and Feature Co-Clustering

Friend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, friend recommendation is a difficult task and most social websites use simple friend recommendation algorithms such as similarity and popularity, whose level of accuracy does do not satisfy the majority of users. In this paper we propose a two-stage procedure for more accurate friend recommendation: In the first stage, based on the relationship of different social networks, the Flickr tag network and contact network are aligned to generate a "possible friend list"; In the second stage, making the assumption that "a friend's friends also tend to be friends", co-clustering is applied to the tag and image information of the list to refine the recommendation result in the first stage. Experimental results show that the proposed method achieves good performance and every stage contributes to the recommendation.