Micro-blog is a social network platform that provides us a new communication and information sharing service. It has become more and more important in our daily life. An user can follow his interested friends to expand his social circle throw following relationship. But how to recommend high quality following users is always a difficulty of personalized service. For the issue, a Seeker-Source matrix factorization model based on micro-blog features is proposed in this paper. The algorithm is an improved algorithm which is based on "Seeker-Source". We extracted the characteristics of user's interest from each data source, and then introduced into the matrix factorization model which is suitable for recommending followee friends. Finally, we optimize the model and get the best factor parameter matrix to recommend followee friends. The experimental results carried on real data sets show that the proposed method performs better than the traditional matrix factorization model.