Current location-based next POI recommendation
Shokirkhon Oppokhonov, Seyoung Park, Isaac K. E. Ampomah · Proceedings of the International Conference on Web Intelligence · 2017
Availability of large volume of community contributed location data enables a lot of location providing services and these services have attracted many industries and academic researchers by its importance. In this paper we propose the new recommender system that recommends the new POI for next hours. First we find the users with similar check-in sequences and depict their check-in sequences as a directed graph, then find the users current location. To recommend the new POI recommendation for next hour we refer to the directed graph we have created. Our algorithm considers both the temporal factor i.e., recommendation time, and the spatial(distance) at the same time. We conduct an experiment on random data collected from Foursquare and Gowalla. Experiment results show that our proposed model outperforms the collaborative-filtering based state-of-the-art recommender techniques.