Personalized recommendation of collective points-of-interest with preference and context awareness.

• A collective POI recommendation model is given for visiting initial and next POIs. • The initial POI recommendation enables a joint modeling of various factors. • The next POI recommendation applies transfer probability and context information. [Display omitted] With the popularity of mobile devices, location-based services, such as Foursquare and Facebook, have attracted increasing attention in recent years. Users share their interests anytime, anywhere with their friends on the social network. Point-of-Interest (POI) recommendation, as one of key services on Location-Based Social Networks (LBSNs), can effectively enhance users' experience especially when they travel in a new city. Previous studies have made great success on POI recommendation by employing geographical influence and user preference. However, it is believed that the human decision on where to visit is very complex and involves the comprehensive factors such as POI popularity, POI location, user trajectory, and time context. In this paper, we propose a collective POIs recommendation framework which leverages the individual latent preference and contextual information. Firstly, to recommend top-K initial POIs, a scoring prediction model is constructed, which considers the influence of similarity, popularity and location of POIs. Furthermore, a next POI recommendation model based on personalized transfer probability is proposed, and the initial POI recommendation is combined to calculate the user's score on the next POI. Extensive experiments based on real datasets collected from Foursquare demonstrate the proposed framework outperforms the state-of-art ones. [ABSTRACT FROM AUTHOR]