Preference-Aware Successive POI Recommendation with Spatial and Temporal Influence

There have been vast advances and rapid growth in Location based social networking (LBSN) services in recent years. Point of Interest (POI) recommendation is one of the most important applications in LBSN services. POI recommendation provides users personalized location recommendation. It helps users to explore new locations and filter uninteresting places that do not match with their interests. But traditional POI recommendation cannot suggest where a user may go the next day or next hour based on their current location or status. In this paper, we consider the task of personalized successive POI recommendation, recommending to a user the very next location where he might be interested to go next based on his current location. Multiple factors influence users to choose a POI, such as user’s categorical preferences, temporal activities and location preferences, popularity of a POI as well as sequential patterns of a user. In this work, we define a unified framework that takes all these factors into consideration to build a better successive POI recommendation model. We evaluate our system with a real-world dataset collected from Foursquare. Experimental results show that our proposed framework works better than other baseline approaches.