Abstract: Most of existing friend recommendation methods only utilize user friendship or content information, and hence they are hard to obtain better recommendation quality. Aiming at this problem, Friend Recommendation using Nonnegative Matrix Factorization (FRNMF) for friend recommendation based on Nonnegative Matrix Factorization (NMF) is proposed, which is fit for data clustering and data reduction. FRNMF adopts user clusters as the core component of its framework. It firstly clusters users by utilizing user friendship network and user-generated content information respectively, and then calculates user pairwise similarities for recommendation based on the cluster results. It can use both user friendship and content information, and it has linear time complexity. FRNMF can alleviate the problem of data sparsity, which can result in the low recommendation quality. By developing protosystem of FRNMF and conducting comparative experiments on Weibo and Scholat social networks, the results show that our method performs better than traditional friend recommendation methods. Moreover, by experimental analysis, moderate increase of the weight of user friendship information can further improve the recommendation quality.