The way Twitter and other microblogging networks work is to have users create follow links among one another, and create short messages to their followers. Most of the time, the creation of follow links to other users does not require approval from the latter. Therefore, it is very easy for a user to create such links. On the other hand, the same cannot be said for seeking incoming follow links which is useful in some application scenarios. In this paper, we therefore study the Follow Link Seeking Problem that aims to find the strategies for a source user to maximize the likelihood of receiving a follow link from a target user. We formulate this problem as a recommendation task and generate a set of strategies from well known follow link patterns. Using the confidence scores of follow link patterns, we derive the success probability of each strategy. Finally, we present Friender, a working recommender system for follow link seeking strategies. The system performs localized crawling of the target user, computes the required strategies on the fly, and presents the strategies visually.