This paper summarized four types of recommendation-related user information from micro-blog system: the user content(UC),the personal information(PI),the interaction(IA)and the social topological information (ST).Based on the four types of information,a user recommendation framework using learning-to-rank technology is built in the paper.Experiment results show:(1)using several features to recommend usually get a better result than using a single feature;(2)recommendation performance based on UC,PI,IA respectively is better than that based on UC.
