Personalized point-of-interest recommendation by mining users' preference transition

ABSTRACT

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.

References

1. C. Cheng, H. Yang, I. King, and M. R. Lyu. Fused matrix factorization with geographical and social influence in location-based social networks. In Proceedings of the 26th AAAI, 2012.
2. S. Gambs, M.-O. Killijian, and M. N. n. del Prado Cortez. Next place prediction using mobility markov chains. In Proceedings of the First Workshop on Measurement, Privacy, and Mobility, 2012.
3. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, 2009.
4. W. Li, C. Eickhoff, and A. P. de Vries. Want a coffee?: predicting users' trails. In Proceedings of the 35th ACM SIGIR, 2012.
5. X. Liu and K. Aberer. Soco: A social network aided context-aware recommender system. In Proceedings of the 22nd WWW, 2013.
6. H. Ma, H. Cao, Q. Yang, E. Chen, and J. Tian. A habit mining approach for discovering similar mobile users. In Proceedings of the 21st WWW, 2012.
7. W. Mathew, R. Raposo, and B. Martins. Predicting future locations with hidden markov models. In Proceedings of ACM UbiComp, 2012.
8. A. Noulas, S. Scellato, N. Lathia, and C. Mascolo. Mining user mobility features for next place prediction in location-based services. In Proceedings of the 12th IEEE ICDM, 2012.
9. A. Noulas, S. Scellato, N. Lathia, and C. Mascolo. A random walk around the city: New venue recommendation in location-based social networks. In Proceedings of IEEE SocialCom, 2012.
10. M. Ye, P. Yin, W.-C. Lee, and D.-L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th ACM SIGIR, 2011.
11. V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang. Towards mobile intelligence: Learning from gps history data for collaborative recommendation. Artif. Intell., 184--185:17--37, 2012.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction