Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Next place prediction using mobility Markov chains
Proceedings of the First Workshop on Measurement, Privacy, and Mobility
A habit mining approach for discovering similar mobile users
Proceedings of the 21st international conference on World Wide Web
Towards mobile intelligence: Learning from GPS history data for collaborative recommendation
Artificial Intelligence
Want a coffee?: predicting users' trails
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Predicting future locations with hidden Markov models
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks
SOCIALCOM-PASSAT '12 Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
Mining User Mobility Features for Next Place Prediction in Location-Based Services
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
SoCo: a social network aided context-aware recommender system
Proceedings of the 22nd international conference on World Wide Web
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Location-based social networks (LBSNs) offer researchers rich data to study people's online activities and mobility patterns. One important application of such studies is to provide personalized point-of-interest (POI) recommendations to enhance user experience in LBSNs. Previous solutions directly predict users' preference on locations but fail to provide insights about users' preference transitions among locations. In this work, we propose a novel category-aware POI recommendation model, which exploits the transition patterns of users' preference over location categories to improve location recommendation accuracy. Our approach consists of two stages: (1) preference transition (over location categories) prediction, and (2) category-aware POI recommendation. Matrix factorization is employed to predict a user's preference transitions over categories and then her preference on locations in the corresponding categories. Real data based experiments demonstrate that our approach outperforms the state-of-the-art POI recommendation models by at least 39.75% in terms of recall.