Context-Aware Point Of Interest Recommendation Using Tensor Factorization

The wide adoption of Location Based Social Networks along with advances in mobile technology, has brought forth as a core service the analysis of large volumes of location-based data for personalized Point of Interest (POIs) recommendations. The majority of the existing recommendation systems take advantage of Collaborative Filtering, but they fail to exploit the contextual information involved with POI checkins (i.e.,, POI category, location, or the checkin timestamp). In this paper we propose CoTF, a Context-Aware Point of Interest Recommendation system using Tensor Factorization, that aims at enhancing the user experience by providing personalized context aware POI recommendations. Our approach exploits Category-based context related to checkins without the need of any pre-or post-filtering techniques. Our detailed experimental evaluation using real data from the Foursquare location-based social network illustrates that our approach can efficiently produce personalized recommendations to users, while significantly reducing the training time compared to current state-of-the-art methods.