Abstract
The pervasive employments of Location-based Social Network call for precise and personalized Point-of-Interest (POI) recommendation to predict which places the users prefer. Modeling user mobility, as an important component of understanding user preference, plays an essential role in POI recommendation. However, existing methods mainly model user mobility through analyzing the check-in data and formulating a distribution without considering why a user checks in at a specific place from psychological perspective. In this paper, we propose a POI recommendation algorithm modeling user mobility by considering check-in data and geographical information. Specifically, with check-in data, we propose a novel probabilistic latent factor model to formulate user psychological behavior from the perspective of utility theory, which could help reveal the inner information underlying the comparative choice behaviors of users. Geographical behavior of all the historical check-ins captured by a power law distribution is then combined with probabilistic latent factor model to form the POI recommendation algorithm. Extensive evaluation experiments conducted on two real-world datasets confirm the superiority of our approach over state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. In: Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)
Tobler, W.R.: A computer movie simulating urban growth in the detroit region. Econ. Geogr. 46, 234–240 (1970)
Deng, S., Huang, L., Xu, G.: Social network-based service recommendation with trust enhancement. Expert Syst. Appl. 41(18), 8075–8084 (2014)
Gao, H., Tang, J., Hu, X., Liu, H.: Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 93–100. ACM (2013)
Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397–406. ACM (2009)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2001)
Li, X., Xu, G., Chen, E., Zong, Y.: Learning recency based comparative choice towards point-of-interest recommendation. Expert Syst. Appl. 42(9), 4274–4283 (2015)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Liu, B., Fu, Y., Yao, Z., Xiong, H.: Learning geographical preferences for point-of-interest recommendation. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1043–1051. ACM (2013)
Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2007)
Noulas, A., Scellato, S., Lathia, N., Mascolo, C.: Mining user mobility features for next place prediction in location-based services. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 1038–1043. IEEE (2012)
Thurstone, L.L.: A law of comparative judgment. Psychol. Rev. 34(4), 273 (1927)
Train, K.E.: Discrete Choice Methods with Simulation. Cambridge University Press, New York (2009)
Yang, S.H., Long, B., Smola, A.J., Zha, H., Zheng, Z.: Collaborative competitive filtering: learning recommender using context of user choice. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 295–304. ACM (2011)
Ye, M., Yin, P., Lee, W.C.: Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 458–461. ACM (2010)
Ye, M., Yin, P., Lee, W.C., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 325–334. ACM (2011)
Zhang, J.D., Chow, C.Y., Li, Y.: igeorec: A personalized and efficient geographical location recommendation framework. IEEE Trans. Serv. Comput. 8(5), 701–714 (2015)
Acknowledgments
This research was partially supported by grants from the Science and Technology Program for Public Wellbeing of China (Grant No. 2013GS340302), National Natural Science Fund Project of China (Grant No. 61232018 and 61325010), National Social and Science Fund project of China (Grant No. 15BGL048), National 863 Plan Project of China (Grant No. 2015AA015403) and Hubei Province Support project of China (Grant No. 2015BAA072).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Chen, Y., Li, X., Li, L., Liu, G., Xu, G. (2016). Modeling User Mobility via User Psychological and Geographical Behaviors Towards Point of-Interest Recommendation. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, X., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9642. Springer, Cham. https://doi.org/10.1007/978-3-319-32025-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-32025-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32024-3
Online ISBN: 978-3-319-32025-0
eBook Packages: Computer ScienceComputer Science (R0)