Microblog services let users broadcast brief textual messages to people who "follow" their activity. Often these posts contain terms called hashtags, markers of a post's meaning, audience, etc. This poster treats the following problem: given a user's stated topical interest, retrieve useful hashtags from microblog posts. Our premise is that a user interested in topic x might like to find hashtags that are often applied to posts about x. This poster proposes a language modeling approach to hashtag retrieval. The main contribution is a novel method of relevance feedback based on hashtags. The approach is tested on a corpus of data harvested from twitter.com.