Boosting point-of-interest recommendation with multigranular time representations

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. © j.ucs.