Different taxi drivers may have different driving preferences when they are cruising to pick up passengers. In this paper, we study the behaviors of taxi drivers' finding passengers with three recommender algorithms, and then provide the taxi driver with the personalized recommendation based on his preferences to the pick-up locations. First, we use the algorithm based on users and collaborative filter of projects to recommend pick-up locations for the taxi drivers. The algorithm is verified by the accuracy rate, proving the feasibility of the two algorithms. Next, taking into account the time factor which would affect the taxies' pickup behavior, we add the time factor into the two algorithms above. Finally, propose the latent factor model (LFM) that breaks the taxi-pickup matrix into two simpler matrices that will help the analysis of the preferences. The results show the three algorithms can effectively form recommendation, and the LFM has a higher accuracy rate.