Recommender systems have been widely used in social networking sites. In this thesis, we propose a novel approach to recommend new followees to Twitter users by learning their historic friends-adding patterns. Based on a user’s past social graph and her interactions with other connected users, scores based on some of the commonly used recommendation strategies are calculated and passed into the learning machine along with the recently added list of followees of the user. Learning to rank algorithm then identifies the best combination of recommendation strategies the user adopted to add new followees in the past. Although users may not adopt any recommendation strategies explicitly, they may subconsciously or implicitly use some. If the actually added followees match with the ones suggested by the recommendation strategy, we consider users are implicitly using that strategy. The experiment using the real data collected from Twitter proves the effectiveness of the proposed approach.