GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation

ABSTRACT

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive locations. However, extreme sparsity of user-POI matrices creates a severe challenge. To cope with this challenge, viewing mobility records on location-based social networks (LBSNs) as implicit feedback for POI recommendation, we first propose to exploit weighted matrix factorization for this task since it usually serves collaborative filtering with implicit feedback better. Besides, researchers have recently discovered a spatial clustering phenomenon in human mobility behavior on the LBSNs, i.e., individual visiting locations tend to cluster together, and also demonstrated its effectiveness in POI recommendation, thus we incorporate it into the factorization model. Particularly, we augment users' and POIs' latent factors in the factorization model with activity area vectors of users and influence area vectors of POIs, respectively. Based on such an augmented model, we not only capture the spatial clustering phenomenon in terms of two-dimensional kernel density estimation, but we also explain why the introduction of such a phenomenon into matrix factorization helps to deal with the challenge from matrix sparsity. We then evaluate the proposed algorithm on a large-scale LBSN dataset. The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon into matrix factorization improves recommendation performance.

References

1. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Know. Data. Eng., 17(6):734--749, 2005.
2. C. Cheng, H. Yang, I. King, and M. Lyu. Fused matrix factorization with geographical and social influence in location-based social networks. In Proceedings of AAAI'12, 2012.
3. C. Cheng, H. Yang, M. R. Lyu, and I. King. Where you like to go next: successive point-of-interest recommendation. In Proceedings of IJCAI'13, pages 2605--2611. AAAI Press, 2013.
4. V. Franc, V. Hlavác, and M. Navara. Sequential coordinate-wise algorithm for the non-negative least squares problem. In Computer Analysis of Images and Patterns, pages 407--414. Springer, 2005.
5. H. Gao, J. Tang, X. Hu, and H. Liu. Exploring temporal effects for location recommendation on location-based social networks. In Proceedings of RecSys'13, pages 93--100. ACM, 2013.
6. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning: data mining, inference, and prediction, 2001.
7. T. Horozov, N. Narasimhan, and V. Vasudevan. Using location for personalized poi recommendations in mobile environments. In Proceedings of SAINT'06. IEEE Computer Society, 2006.
8. Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In Proceedings of ICDM'08, pages 263--272. IEEE, 2008.
9. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, 2009.
10. C. L. Lawson and R. J. Hanson. Solving Least Squares Problems, volume 15. SIAM, 1995.
11. C.-J. Lin. Projected gradient methods for nonnegative matrix factorization. Neural computation, 19(10):2756--2779, 2007.
12. B. Liu, Y. Fu, Z. Yao, and H. Xiong. Learning geographical preferences for point-of-interest recommendation. In Proceedings of KDD'13, pages 1043--1051. ACM, 2013.
13. B. Liu and H. Xiong. Point-of-interest recommendation in location based social networks with topic and location awareness. In Proceedings of SDM'13, pages 396--404. SIAM, 2013.
14. X. Liu, Y. Liu, K. Aberer, and C. Miao. Personalized point-of-interest recommendation by mining users' preference transition. In Proceedings of CIKM'13, pages 733--738. ACM, 2013.
15. H. Ma, C. Liu, I. King, and M. R. Lyu. Probabilistic factor models for web site recommendation. In Proceedings of SIGIR'11, pages 265--274. ACM, 2011.
16. K. P. Murphy. Machine learning: a probabilistic perspective. The MIT Press, 2012.
17. 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 SocialCom'12, pages 144--153. IEEE, 2012.
18. R. Pan, Y. Zhou, B. Cao, N. Liu, R. Lukose, M. Scholz, and Q. Yang. One-class collaborative filtering. In Proceedings of ICDM'08, pages 502--511. IEEE, 2008.
19. M. Park, J. Hong, and S. Cho. Location-based recommendation system using bayesian user's preference model in mobile devices. Ubiquitous Intelligence and Computing, pages 1130--1139, 2007.
20. D. Seung and L. Lee. Algorithms for non-negative matrix factorization. Advances in neural information processing systems, 13:556--562, 2001.
21. W. Tobler. A computer movie simulating urban growth in the detroit region. Economic geography, 46:234--240, 1970.
22. D. Yang, D. Zhang, Z. Yu, and Z. Wang. A sentiment-enhanced personalized location recommendation system. In Proceedings of the 24th ACM Conference on Hypertext and Social Media(HT'13), pages 119--128. ACM, 2013.
23. M. Ye, D. Shou, W. Lee, P. Yin, and K. Janowicz. On the semantic annotation of places in location-based social networks. In Proceedings of KDD'11, pages 520--528. ACM, 2011.
24. M. Ye, P. Yin, W.-C. Lee, and D.-L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of SIGIR'11, pages 325--334. ACM, 2011.
25. H. Yin, Y. Sun, B. Cui, Z. Hu, and L. Chen. Lcars: a location-content-aware recommender system. In Proceedings of KDD'13, pages 221--229. ACM, 2013.
26. J.-D. Zhang and C.-Y. Chow. igslr: Personalized geo-social location recommendation-a kernel density estimation approach. In Proceedings of GIS'13.
27. V. Zheng, Y. Zheng, X. Xie, and Q. Yang. Collaborative location and activity recommendations with gps history data. In Proceedings of WWW'10, pages 1029--1038. ACM, 2010.
28. Y. Zheng, L. Zhang, Z. Ma, X. Xie, and W. Ma. Recommending friends and locations based on individual location history. ACM Trans. Web, 5(1):5, 2011.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Clustering and classification

      • Document filtering

      • Information extraction

  • Information systems applications

    • Data mining

      • Clustering