International Conference on Web Information Systems Engineering

Web Information Systems Engineering – WISE 2015 pp 426-441 | Cite as

Geographical Constraint and Temporal Similarity Modeling for Point-of-Interest Recommendation

  • Huimin Wu
  • Jie Shao
  • Hongzhi Yin
  • Heng Tao Shen
  • Xiaofang Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9419)

Abstract

People often share their visited Points-of-Interest (PoIs) by “check-ins”. On the one hand, human mobility varies with each individual but still implies regularity. Check-ins of an individual tend to localize in a specific geographical range. We propose a novel model to capture personalized geographical constraint of each individual. On the other hand, PoIs reflect requirements of people from different aspects. Usually, places of different functions show different temporal visiting distributions and places of similar function share similar visiting pattern in temporal aspect. Temporal distribution similarity can be used to characterize functional similarity. Based on the findings above, this paper introduces improved collaborative filtering models by jointly taking advantages of geographical constraint and temporal similarity. Experimental results on real data collected from Gowalla and JiePang demonstrate the effectiveness of our models.

Keywords

Recommendation system Collaborative filtering Geographical constraint Temporal similarity 

References

  1. 1.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: SIGKDD, pp. 1082–1090 (2011)Google Scholar
  2. 2.
    Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: SIGKDD, pp. 226–231 (1996)Google Scholar
  3. 3.
    Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: SIGKDD, pp. 831–840 (2014)Google Scholar
  4. 4.
    Lichman, M., Smyth, P.: Modeling human location data with mixtures of kernel densities. In: SIGKDD, pp. 35–44 (2014)Google Scholar
  5. 5.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)Google Scholar
  6. 6.
    Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: Temporal recommendation on graphs via long- and short-term preference fusion. In: SIGKDD, pp. 723–732 (2010)Google Scholar
  7. 7.
    Ye, M., Janowicz, K., Mülligann, C., Lee, W.: What you are is when you are: the temporal dimension of feature types in location-based social networks. In: SIGSPATIAL, pp. 102–111 (2011)Google Scholar
  8. 8.
    Ye, M., Yin, P., Lee, W.: Location recommendation for location-based social networks. In: SIGSPATIAL, pp. 458–461 (2010)Google Scholar
  9. 9.
    Ye, M., Yin, P., Lee, W., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: SIGIR, pp. 325–334 (2011)Google Scholar
  10. 10.
    Yin, H., Zhou, X., Shao, Y., Wang, H., Sadiq, S.: Joint modeling of user check-in behaviors for point-of-interest recommendation. In: CIKM (2015)Google Scholar
  11. 11.
    Yuan, Q., Cong, G., Ma, Z., Sun, A., Magnenat-Thalmann, N.: Time-aware point-of-interest recommendation. In: SIGIR, pp. 363–372 (2013)Google Scholar
  12. 12.
    Yuan, Q., Cong, G., Ma, Z., Sun, A., Magnenat-Thalmann, N.: Who, where, when and what: discover spatio-temporal topics for twitter users. In: SIGKDD, pp. 605–613 (2013)Google Scholar
  13. 13.
    Yuan, Q., Cong, G., Sun, A.: Graph-based point-of-interest recommendation with geographical and temporal influences. In: CIKM, pp. 659–668 (2014)Google Scholar
  14. 14.
    Zhang, J., Chow, C.: iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. In: SIGSPATIAL, pp. 324–333 (2013)Google Scholar
  15. 15.
    Zhang, J., Chow, C.: GeoSoCa: exploiting geographical, social and categorical correlations for point-of-interest recommendations. In: SIGIR, pp. 443–452 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Huimin Wu
    • 1
  • Jie Shao
    • 1
  • Hongzhi Yin
    • 2
  • Heng Tao Shen
    • 2
  • Xiaofang Zhou
    • 2
  1. 1.University of Electronic Science and Technology of ChinaChengduChina
  2. 2.The University of QueenslandBrisbaneAustralia

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