Location Recommendation Based on Periodicity of Human Activities and Location Categories

  • Seyyed Mohammadreza Rahimi
  • Xin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)


Location recommendation is a popular service for location-based social networks. This service suggests unvisited sites to the users based on their visiting history and site information. In this paper, we first present how to build the temporal and spatial probability distribution functions (PDF) to model the temporal and spatial checkin behavior of the users. Then we propose two recommender algorithms, Probabilistic Category Recommender (PCR) and Probabilistic Category-based Location Recommender (PCLR), based on the periodicity of user checkin behavior. PCR uses the temporal PDF to model the periodicity of users’ checkin behavior. PCLR combines the temporal category model used in PCR with a geographical influence model built on the spatial PDF. The experimental results show that the proposed methods achieve better precision and recall than two well-known location recommendation methods.


Recommender system Location-based Social Networks Location- Category probability model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ye, M., Ying, P., Lee, W., Lee, D.: Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation. In: 34th ACM International Conference on Research and Development on Information Retrieval, Beijing, China, pp. 325–344 (2011)Google Scholar
  2. 2.
    Cho, E., Myers, S., Leskovec, J.: Friendship and Mobility: User Movement In Location-Based Social Networks. In: 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Diego, California, USA, pp. 1082–1090 (2011)Google Scholar
  3. 3.
    Cheng, Z., Caverlee, J., Lee, K., Sui, D.: Exploring millions of footprints in location sharing services. In: 5th International Conference on Weblogs and Social Media, Barcelona, Spain, pp. 81–88 (2011)Google Scholar
  4. 4.
    Zhou, D., Wang, B., Rahimi, S.M., Wang, X.: A Study of Recommending Locations on Location-Based Social Network by Collaborative Filtering. In: Kosseim, L., Inkpen, D. (eds.) Canadian AI 2012. LNCS, vol. 7310, pp. 255–266. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative Location and Activity Recommendations with GPS History Data. In: 19th International Conference on World Wide Web, Raleigh, North Carolina, USA, pp. 1029–1038 (2010)Google Scholar
  6. 6.
    Park, M.-H., Hong, J.-H., Cho, S.-B.: Location-Based Recommendation System Using Bayesian User’s Preference Model in Mobile Devices. In: Indulska, J., Ma, J., Yang, L.T., Ungerer, T., Cao, J. (eds.) UIC 2007. LNCS, vol. 4611, pp. 1130–1139. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Simon, R., Frőhlich, P.: A Mobile Application Framework for the Geospatial Web. In: 16th International Conference on World Wide Web, Banff, Alberta, Canada, pp. 381–390 (2007)Google Scholar
  8. 8.
    Beeharee, A., Steed, A.: Exploiting Real World Knowledge in Ubiquitous Applications. Personal and Ubiquitous Computing Archive 11(6), 429–437 (2007)CrossRefGoogle Scholar
  9. 9.
    Wang, J., Prabhala, B.: Periodicity Based Next Place Prediction. In: Workshop on Mobile Data Challenge by Nokia, Newcastle, UK (2012)Google Scholar
  10. 10.
    Bao, J., Zheng, Y., Mokbel, M.: Location-based and Preference-Aware Recommendation Using Sparse Geo-Social Networking Data. In: 20th ACM SIGSPATIAL International Conference on Advances in GIS. Redondo Beach, California (2012)Google Scholar
  11. 11.
    Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, pp. 1099–1108 (2010)Google Scholar
  12. 12.
    Li, Z., Wang, J., Han, J.: Mining event periodicity from incomplete observations. In: 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Beijing, China, pp. 444–452 (2012)Google Scholar
  13. 13.
    Eagle, N., Pentland, A.: Eigenbehaviors: identifying structure in routine. Behavioral Ecology and Sociobiology 63, 1057–1066 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Seyyed Mohammadreza Rahimi
    • 1
  • Xin Wang
    • 1
  1. 1.Department of Geomatics Engineering, Schulich School of EngineetingUniversity of CalgaryCalgaryCanada

Personalised recommendations