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A Spatial User Similarity Measure for Geographic Recommender Systems

  • Conference paper
GeoSpatial Semantics (GeoS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5892))

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Abstract

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.

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References

  1. Goodchild, M.: Citizens as sensors: the world of volunteered geography. GeoJournal 69(4), 211–221 (2007)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. Rattenbury, T., Naaman, M.: Methods for extracting place semantics from flickr tags. ACM Trans. Web 3(1), 1–30 (2009)

    Article  Google Scholar 

  9. Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. Int. J. Comput. Vision 80(2), 189–210 (2008)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  11. Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  18. Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)

    Article  Google Scholar 

  19. Tversky, A.: Features of similarity. Psychological Review 84, 327–352 (1977)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  22. Schwering, A.: Approaches to semantic similarity measurement for geo-spatial data: A survey. Transactions in GIS 12(1), 5–29 (2008)

    Article  Google Scholar 

  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. 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)

    MATH  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  27. Tanimoto, T.T.: An Elementary Mathematical Theory of Classification and Prediction (1958)

    Google Scholar 

  28. Rosch, E.: Principles of Categorization, pp. 27–48. John Wiley & Sons Inc., Chichester (1978)

    Google Scholar 

  29. Guy, M., Tonkin, E.: Folksonomies: Tidying up tags? D-Lib Magazine 12 (2006)

    Google Scholar 

  30. Anderson, C.: The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion (2006)

    Google Scholar 

  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)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratory for Semantic Information Technology, University of Bamberg, Feldkirchenstrasse 21, 96052, Bamberg, Germany

    Christian Matyas & Christoph Schlieder

Authors
  1. Christian Matyas
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  2. Christoph Schlieder
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Editor information

Editors and Affiliations

  1. Department of Geography, The Pennsylvania State University, University Park, 16802, PA, USA

    Krzysztof Janowicz

  2. Department of Geography, University of California, Santa Barbara, 93106, CA, USA

    Martin Raubal

  3. Centro de Investigacion en Computacion, National Polytechnic Institute, 07738, Mexico City, Mexico

    Sergei Levashkin

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© 2009 Springer-Verlag Berlin Heidelberg

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Matyas, C., Schlieder, C. (2009). A Spatial User Similarity Measure for Geographic Recommender Systems. In: Janowicz, K., Raubal, M., Levashkin, S. (eds) GeoSpatial Semantics. GeoS 2009. Lecture Notes in Computer Science, vol 5892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10436-7_8

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  • DOI: https://doi.org/10.1007/978-3-642-10436-7_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10435-0

  • Online ISBN: 978-3-642-10436-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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