Recommendations Based on Region and Spatial Profiles

  • Gavin McArdle
  • Mathieu Petit
  • Cyril Ray
  • Christophe Claramunt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7236)


Fuelled by the quantity of available online spatial data that continues to grow, the requirement for filtering spatial content to match mobile users’ context becomes increasingly important. This paper introduces a flexible algorithm to derive users’ preferences in a mobile and distributed system. Such preferences are implicitly computed from users’ virtual and physical interactions with spatial features. Using this concept, region profiles for specific spatial contexts can be generated and used to recommend content to those visiting that region. Our approach provides a set of profiles (personal and region-based) which are combined to adapt the presentation of a given service to suit users’ immediate needs and interests. A proposed college campus navigation assistant illustrates the benefits of such an unobtrusive recommender system.


Location-based services Contextual adaptation Implicit profiling Multi-user recommendations 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  2. 2.
    Ballatore, A., McArdle, G., Kelly, C., Bertolotto, M.: Recomap: An interactive and adaptive map-based recommender. In: Proceedings of the 25th ACM Symposium on Applied Computing (SAC), pp. 887–891. ACM (2010)Google Scholar
  3. 3.
    Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    Gartner, G., Cartwright, W.E., Peterson, M.P. (eds.): Location-Based Services and TeleCartography. Lecture Notes in Geoinformation and Cartography. Springer, Heidelberg (2007)Google Scholar
  5. 5.
    Gartner, G., Rehrl, K. (eds.): Location-Based Services and TeleCartography II. Lecture Notes in Geoinformation and Cartography. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Gupta, G., Lee, W.-C.: Collaborative spatial object recommendation in location based services. In: International Conference on Parallel Processing Workshops, pp. 24–33 (2010)Google Scholar
  7. 7.
    Kelly, D., Teevan, J.: Implicit feedback for inferring user preference: a bibliography. ACM SIGIR Forum 37(2), 18–28 (2003)CrossRefGoogle Scholar
  8. 8.
    Lam, W., Mukhopadhyay, S., Mostafa, J., Palakal, M.: Detection of shifts in user interests for personalized information filtering. In: SIGIR 1996: Proc. of the 19th International Conference on Research and Development in Information Retrieval, pp. 317–325. ACM (1996)Google Scholar
  9. 9.
    Longley, P.A., Goodchild, M.F., Maguire, D.J., Rhind, D.W.: Geographical Information Systems and Sciences, 2nd edn., 517 pages. John Wiley and Sons (2005)Google Scholar
  10. 10.
    Mac Aoidh, E., Bertolotto, M., Wilson, D.C.: Understanding geospatial interests by visualising map interaction behaviour. Information Visualization 7(3-4), 257–286 (2008)CrossRefGoogle Scholar
  11. 11.
    Mac Aoidh, E., McArdle, G., Petit, M., Ray, C., Bertolotto, M., Claramunt, C., Wilson, D.: Personalization in adaptive and interactive gis. Annals of GIS 11(1), 23–33 (2009)CrossRefGoogle Scholar
  12. 12.
    Petit, M., Ray, C., Claramunt, C.: A user context approach for adaptive and distributed GIS. In: Proceedings of the 10th International Conference on Geographic Information Science (AGILE 2007), Aalborg, Denmark. Lecture Notes in Geoinformation and Cartography, pp. 121–133. Springer, Heidelberg (2007)Google Scholar
  13. 13.
    Rich, E.: Users are individuals: individualizing user models. Interntational Journal of Man-Machine Studies 18(3), 199–214 (1983)CrossRefGoogle Scholar
  14. 14.
    Satyanarayanan, M.: Pervasive computing: Vision and challenges. IEEE Personal Communications 8(4), 10–17 (2001)CrossRefGoogle Scholar
  15. 15.
    Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 253–260. ACM, New York (2002)CrossRefGoogle Scholar
  16. 16.
    Tahir, A., McArdle, G., Ballatore, A., Bertolotto, M.: Collaborative filtering - a group profiling algorithm for personalisation in a spatial recommender system. In: Geoinformatik 2010 (2010)Google Scholar
  17. 17.
    Wilson, D., Doyle, J., Weakliam, J., Bertolotto, M., Lynch, D.: Personalized maps in multimodal GIS. International Journal of Web Emerging Technology 3(2), 196–216 (2007)CrossRefGoogle Scholar
  18. 18.
    Wu, D., Zhao, D., Zhang, X.: An adaptive user profile based on memory model. In: Web-Age Information Management, pp. 461–468 (2008)Google Scholar
  19. 19.
    Yang, Y., Claramunt, C.: A Hybrid Approach for Spatial Web Personalization. In: Li, K.-J., Vangenot, C. (eds.) W2GIS 2005. LNCS, vol. 3833, pp. 206–221. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gavin McArdle
    • 1
  • Mathieu Petit
    • 2
  • Cyril Ray
    • 3
  • Christophe Claramunt
    • 3
  1. 1.National Centre for GeocomputationNational University of Ireland MaynoothMaynoothIreland
  2. 2.Matiasat System R&DLevallois-PerretFrance
  3. 3.Naval Academy Research InstituteBrestFrance

Personalised recommendations