Technologies of recommender systems are being increasingly adopted by Location Based Social Networks (LBSNs) with the purpose of recommending Pointsof-Interest (POIs) to their users, and different contextual characteristics have been incorporated to enhance this process. Among these characteristics, the time at which users express their preferences (typically, by checking-in to different POIs) and ask for recommendations, is frequently referred as a first-order feature in this process. However, even when its influence on improving the accuracy of recommendations has been empirically demonstrated, time is still mainly considered through a monogranular representation (one-hour or one-day blocks). In this article, we introduce a POI recommendation approach based on a multigranular characterization of time, composed of hour, day-of-the-week, and month. Based on this concept, we propose two representations of user check-ins: one that directly extends a monogranular proposal of time for POI recommendations, and other based on a statistical representation of check-in distributions in time. For both representations, corresponding algorithms to compute user similarity and preference prediction are introduced. The experimental evaluation shows promising results in terms of accuracy and scalability.