POI Recommendations to users based on this paper[https://www.ntu.edu.sg/home/axsun/paper/sun_sigir13quan.pdf] (broken link replacement - https://dl.acm.org/doi/abs/10.1145/2484028.2484030)
The availability of user check-in data in large volume from therapid growing location-based social networks (LBSNs) enables manyimportant location-aware services to users. Point-of-interest (POI)recommendation is one of such services, which is to recommendplaces where users have not visited before. Several techniques havebeen recently proposed for the recommendation service. However,no existing work has considered the temporal information for POIrecommendations in LBSNs. We believe that time plays an impor-tant role in POI recommendations because most users tend to visitdifferent places at different time in a day,e.g.,visiting a restaurantat noon and visiting a bar at night. In this paper, we define a newproblem, namely, thetime-aware POI recommendation, to recom-mend POIs for a given user at a specified time in a day. To solvethe problem, we develop a collaborative recommendation modelthat is able to incorporate temporal information. Moreover, basedon the observation that users tend to visit nearby POIs, we furtherenhance the recommendation model by considering geographicalinformation. Our experimental results on two real-world datasetsshow that the proposed approach outperforms the state-of-the-artPOI recommendation methods substantially.