Location-Based Social Networks (LBSNs) are social networks with functionalities that let users share their location information with other users. Location recommendation is the task of suggesting unvisited locations to the users. A good location recommender should make user-specific recommendations based on users’ preferences, geographical constraints and time. In this thesis we investigate the development of two novel location recommendation methods for Location-Based Social Networks (LBSNs), the Probabilistic Category-based Location Recommender (PCLR) and the Behavior-based Location Recommender (BLR). The PCLR method finds the temporal and spatial patterns of users’ activities in the form of temporal and spatial probability distributions. It then uses the patterns to select the right category of locations and recommend nearby locations of that type to the user. On the other hand, the BLR method first extracts user behaviors from their check-in history. It then utilizes a collaborative filtering technique to extract common behaviors and predict behavior of the user at a given time. Finally, BLR filters locations in the user’s proximity based on the predicted behavior when making the location recommendation. PCLR and BLR methods go through a set of experiments on a real-world check-in dataset. These experiments show that PCLR and BLR methods improve the performance of the existing location recommenders in terms of precision and recall. Additionally, the BLR method produces much better recommendations for the cold-start users.