A recommendation algorithm for point of interest using time-based collaborative filtering

Location-based social networks (LBSNs) make it possible for people to share their visited places by uploading the check-in information. To improve the efficiency of recommendation algorithm, researchers introduce check-in data into point of interest (POI) recommendation to help users find new and interesting place. However, some researches ignore the signification of time factor for POI recommendation in LBSNs. In this paper, we propose a time-based collaborative filtering algorithm according to the similarity between users which combines the global similarity during a long period and local similarity within a short time interval. The experimental results show that the method we proposed can get more accurate recommendation.