The primary aim of location recommendation is to predict users’ future movement by modeling user preference. Multiple types of information have been adopted in profiling users; however, simultaneously combining them for a better recommendation is challenging. In this study, a novel location recommendation method that incorporates geographical, categorical, and social preferences with location popularity is proposed. Experimental results on two public datasets show that the proposed method significantly outperforms two state-of-the-art recommendation methods. Geographical preference generally shows more importance than both categorical and social preferences. A category hierarchy that unleashes the independent assumption of location tags improves categorical preference. Location popularity proves to be a useful metric in ranking candidate locations. The findings of this study can provide practical guidelines for location recommendation services.