A Spatial User Similarity Measure for Geographic Recommender Systems

  • Christian Matyas
  • Christoph Schlieder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5892)


Recommender systems solve an information filtering task. They suggest data objects that seem likely to be relevant to the user based upon previous choices that this user has made. A geographic recommender system recommends items from a library of georeferenced objects such as photographs of touristic sites. A widely-used approach to recommending consists in suggesting the most popular items within the user community. However, these approaches are not able to handle individual differences between users. We ask how to identify less popular geographic objects that are nevertheless of interest to a specific user. Our approach is based on user-based collaborative filtering in conjunction with an prototypical model of geographic places (heatmaps). We discuss four different measures of similarity between users that take into account the spatial semantic derived from the spatial behavior of a user community. We illustrate the method with a real-world use case: recommendations of georeferenced photographs from the public website Panoramio. The evaluation shows that our approach achieves a better recall and precision for the first ten items than recommendations based on the most popular geographic items.


recommendation personalization geospatial services 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goodchild, M.: Citizens as sensors: the world of volunteered geography. GeoJournal 69(4), 211–221 (2007)CrossRefGoogle Scholar
  2. 2.
    Scharl, A., Tochtermann, K., Jain, L., Wu, X.: The Geospatial Web: How Geobrowsers, Social Software and the Web 2.0 are Shaping the Network Society. Springer-11645 /Dig. Serial. Springer, London (2007)Google Scholar
  3. 3.
    Schlieder, C.: Modeling collaborative semantics with a geographic recommender. In: Hainaut, J.-L., Rundensteiner, E.A., Kirchberg, M., Bertolotto, M., Brochhausen, M., Chen, Y.-P.P., Cherfi, S.S.-S., Doerr, M., Han, H., Hartmann, S., Parsons, J., Poels, G., Rolland, C., Trujillo, J., Yu, E., Zimányie, E. (eds.) ER Workshops 2007. LNCS, vol. 4802, pp. 338–347. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Schlieder, C., Matyas, C.: Photographing a city: An analysis of place concepts based on spatial choices. Spatial Cognition & Computation 9(3), 212–228 (2009)CrossRefGoogle Scholar
  5. 5.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: CSCW 1994: Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186. ACM, New York (1994)CrossRefGoogle Scholar
  6. 6.
    Girardin, F., Fiore, F.D., Blat, J., Ratti, C.: Understanding of tourist dynamics from explicitly disclosed location information. In: The 4th International Symposium on LBS & TeleCartography (2007)Google Scholar
  7. 7.
    Ahern, S., Naaman, M., Nair, R., Yang, J.H.I.: World explorer: visualizing aggregate data from unstructured text in geo-referenced collections. In: JCDL 2007: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, pp. 1–10. ACM, New York (2007)Google Scholar
  8. 8.
    Rattenbury, T., Naaman, M.: Methods for extracting place semantics from flickr tags. ACM Trans. Web 3(1), 1–30 (2009)CrossRefGoogle Scholar
  9. 9.
    Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. Int. J. Comput. Vision 80(2), 189–210 (2008)CrossRefGoogle Scholar
  10. 10.
    Simon, I., Seitz, S.M.: Scene segmentation using the wisdom of crowds. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 541–553. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)zbMATHCrossRefGoogle Scholar
  12. 12.
    McLaughlin, M.R., Herlocker, J.L.: A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In: SIGIR 2004: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 329–336. ACM, New York (2004)Google Scholar
  13. 13.
    Zhang, J., Pu, P.: A recursive prediction algorithm for collaborative filtering recommender systems. In: RecSys 2007: Proceedings of the 2007 ACM conference on Recommender systems, pp. 57–64. ACM, New York (2007)CrossRefGoogle Scholar
  14. 14.
    Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: CIKM 2001: Proceedings of the tenth international conference on Information and knowledge management, pp. 247–254. ACM, New York (2001)CrossRefGoogle Scholar
  15. 15.
    Park, Y.J., Tuzhilin, A.: The long tail of recommender systems and how to leverage it. In: RecSys 2008: Proceedings of the 2008 ACM conference on Recommender systems, pp. 11–18. ACM, New York (2008)CrossRefGoogle Scholar
  16. 16.
    Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: RecSys 2008: Proceedings of the 2008 ACM conference on Recommender systems, pp. 123–130. ACM, New York (2008)CrossRefGoogle Scholar
  17. 17.
    Ziegler, C.N., Lausen, G., Schmidt-Thieme, L.: Taxonomy-driven computation of product recommendations. In: CIKM 2004: Proceedings of the thirteenth ACM international conference on Information and knowledge management, pp. 406–415. ACM, New York (2004)CrossRefGoogle Scholar
  18. 18.
    Linden, G., Smith, B., York, J.: recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)CrossRefGoogle Scholar
  19. 19.
    Tversky, A.: Features of similarity. Psychological Review 84, 327–352 (1977)CrossRefGoogle Scholar
  20. 20.
    Jones, C.B., Alani, H., Tudhope, D.: Geographical information retrieval with ontologies of place. In: Montello, D.R. (ed.) COSIT 2001. LNCS, vol. 2205, pp. 322–335. Springer, Heidelberg (2001)Google Scholar
  21. 21.
    Rodríguez, M.A., Egenhofer, M.J.: Comparing geospatial entity classes: An asymmetric and context-dependent similarity measure. International Journal of Geographical Information Science 18, 229–256 (2004)CrossRefGoogle Scholar
  22. 22.
    Schwering, A.: Approaches to semantic similarity measurement for geo-spatial data: A survey. Transactions in GIS 12(1), 5–29 (2008)CrossRefGoogle Scholar
  23. 23.
    Janowicz, K., Raubal, M., Schwering, A., Kuhn, W. (eds.): Special Issue on Semantic Similarity Measurement and Geospatial Applications. Transactions in GIS 12(6) (2008)Google Scholar
  24. 24.
    Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11, 95–130 (1999)zbMATHGoogle Scholar
  25. 25.
    Bae-Hee, L., Heung-Nam, K., Jin-Guk, J., Geun-Sik, J.: Location-based service with context data for a restaurant recommendation. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 430–438. Springer, Heidelberg (2006)Google Scholar
  26. 26.
    Horozov, T., Narasimhan, N., Vasudevan, V.: Using location for personalized poi recommendations in mobile environments. In: SAINT 2006: Proceedings of the International Symposium on Applications on Internet, Washington, DC, USA, pp. 124–129. IEEE Computer Society, Los Alamitos (2006)CrossRefGoogle Scholar
  27. 27.
    Tanimoto, T.T.: An Elementary Mathematical Theory of Classification and Prediction (1958)Google Scholar
  28. 28.
    Rosch, E.: Principles of Categorization, pp. 27–48. John Wiley & Sons Inc., Chichester (1978)Google Scholar
  29. 29.
    Guy, M., Tonkin, E.: Folksonomies: Tidying up tags? D-Lib Magazine 12 (2006)Google Scholar
  30. 30.
    Anderson, C.: The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion (2006)Google Scholar
  31. 31.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Christian Matyas
    • 1
  • Christoph Schlieder
    • 1
  1. 1.Laboratory for Semantic Information TechnologyUniversity of BambergBambergGermany

Personalised recommendations