With the broad application of web 2.0 technology, various kinds of online social networks arise at present. The emergence of social networks not only helps the public to facilitate the sharing and communicating, but also helps them to know more new friends from cyberspace social circle. Therefore, friends recommendation becomes a critical function for various online social networks. Problem of friends recommendation is actually the problem of link prediction in essential, and vast majority of the solutions are traditionally based on the social relation, yet common interests are also the important factor of forming friendships in real life besides social relations. This article proposes a new matrix factorization based method for friends recommendation. The proposed method considers two data sources of social relations and interest ratings simultaneously, utilizes Gaussian kernel to capture interest relevance (interest similarity) between persons, makes use of Gaussian process to generate the users’ profile vectors, and eventually achieves a recommendation method having the capability of commending friends with common interests. Experiments show that our method outperforms the art-of-the-state traditional link prediction methods using only social relations.