A Grid-Based successive point-of-interest recommendation method

With the increasing popularity of location-based social networks (LBSNs), users are able to share the point-of-interests (POIs) they visited by check-ins. By analyzing the users' historical check-in records, POI recommendation can help users get better visiting experiences by recommending POIs which users may be interested in. Although recent successive POI recommendation methods consider geographical influence by measuring distances among POIs, most of them ignore the influence of the regions where the POIs are located. Therefore, we propose a grid-based successive POI recommendation method, named UGSE-LR, to take the regional influence into consideration when recommending POIs. UGSE-LR first splits an area into grids for estimating regional influence. Then, UGSE-LR applies Edge-weighted Personalized PageRank (EdgePPR) for modeling the successive transitions among POIs. Finally, UGSE-LR fuses user preference, regional preference and successive transition preference into a unified recommendation framework. Experimental results on two real LBSN datasets show that our method is more accurate than the state-of-the-art successive POI recommendation methods in terms of precision and recall.