Combining Clustering Algorithm with Factorization Machine for Friend Recommendation in Social Network

Social Network Service (SNS) has been explosively growing and generating huge amounts of data every day, it is a meaningful job to mine useful information from the big data which generated from the social networks. In this paper, we study the relationship and behavior of social network users, and then put forward a model which combines Clustering Algorithm with Factorization Machine (FM) for SNS Friend Recommendation. With the help of Clustering Algorithm, we classified the users and make it easy to locate users´ characteristics and interests, and by using FM we can solve the Data Sparseness problem effectively. We trained this model by Markov Chain Monte Carlo (MCMC) algorithm and verified our model using Ten cent Webo´s real dataset and proved it has a better computational efficiency and better accuracy in recommending friends.