A topic community-based method for friend recommendation inrnlarge-scale online social networks

摘要

Online social networks (OSNs) have become more and more popular and have attracted a great many users.rnFriend recommendation, which is one of the important services in OSN, can help users discover their interestedrnfriends and alleviate the problem of information overload. However, most of existing recommendationrnmethods only consider either user link or content information and hence are not effective enough to providernhigh quality recommendations. In this paper, we propose a topic community-based method via NonnegativernMatrix Factorization (NMF). This method first applies joint NMF model to mine topic communities existingrnin OSN by combing link and content information. Then it computes user pairwise similarities and makesrnfriends recommendation based on topic communities. Furthermore, this method can be implemented usingrnthe MapReduce distributed computing framework. Extensive experiments show that our proposed methodrnnot only has better recommendation performance than state-of-the-art methods but also has good scalabilityrnto deal with the problem of friend recommendation in large-sale OSNs. Moreover, the applicationrncase demonstrates that it can significantly improve friend recommendation service in the real world OSN.rnCopyright © 2016 John Wiley & Sons, Ltd.