Micro-blog recommendation is an effective technique to resolve the information overload problem in micro-blog systems. In this paper, we summarize and model several key factors which affect a user's interest on a specific status, including implicit features, content features (i.e., content similarity, user tags, and user's favorites), social network features, and status features. Based on the above features, we propose a community hot status recommendation algorithm—CMR (community micro-blog recommendation), which combines both explicit features and implicit features for better recommendation. Specifically, we propose a learning method to rank based framework, which learns a user's interest model of status from his preference data, including his retweets, favorites, comments, etc. Then new statuses are scored and ranked using the learned interest model. In order to measure our method's performance, we conduct a series of experiments in three community data sets (including NLP, Photography and Basketball). Experimental results show that: 1)by combining both implicit features and explicit features, our method achieves better recommendation performance than that using a single type of features; 2) compared with the MRR (micro-blog repost rank based recommendation), CMR gets better recommendation performance; 3) MRR prefers recommending hot statuss in the whole micro-blog system, in contrast CMR usually recommends community-specific statuses.