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Using the geographic scopes of web documents for contextual advertising

Published:18 February 2010Publication History

ABSTRACT

Geotargeting is a specialization of contextual advertising where the objective is to target ads to Website visitors concentrated in well-defined areas. Current approaches involve targeting ads based on the physical location of the visitors, estimated through their IP addresses. However, there are many situations where it would be more interesting to target ads based on the geographic scope of the target pages, i.e., on the general area implied by the locations mentioned in the textual contents of the pages. Our proposal applies techniques from the area of geographic information retrieval to the problem of geotargeting. We address the task through a pipeline of processing stages, which involves (i) determining the geographic scope of target pages, (ii) classifying target pages according to locational relevance, and (iii) retrieving ads relevant to the target page, using both textual contents and geographic scopes. Experimental results attest for the adequacy of the proposed methods in each of the individual processing stages.

References

  1. E. Amitay, N. Har'El, R. Sivan, and A. Soffer (2004) Web-a-where: geotagging web content. In Proceedings of the 27th ACM SIGIR Conference on Research and Development in information Retrieval. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. I. Anastácio, B. Martins, P. Calado (2009) Classifying documents according to locational relevance. In Proceedings of EPIA '09: 14th Portuguese Conference on Artificial Intelligence. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. A. Broder, M. Ciaramita, M. Fontoura, E. Gabrilovich, V. Josifovski, D. Metzler, V. Murdock, and V. Plachouras (2008) To Swing or not to Swing: Learning when (not) to Advertise. In Proceeding of the 17th ACM Conference on Information and Knowledge Management. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. G. Cai (2002) GeoVSM: An Integrated Retrieval Model For Geographical Information. In Proceedings of the 2nd International Conference on Geographic Information Science. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. P. Frontiera, R. Larson, and J. Radke (2008) A comparison of geometric approaches to assessing spatial similarity for GIR. International Journal of Geographic Information Sciences, 22(3). Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. L. Gravano, V. Hatzivassiloglou, and R. Lichtenstein (2003) Categorizing web queries according to geographical locality. In Proceedings of the 12th international Conference on information and Knowledge Management. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. C. Guo, Y. Liu, W. Shen, H. Wang, Q. Yu, and Y. Zhang (2009) Mining the Web and the Internet for Accurate IP Address Geolocations. In Proceedings of the 28th IEEE Conference on Computer Communications.Google ScholarGoogle Scholar
  8. R. Jones, W. V. Zhang, B. Rey, P. Jhala, and E. Stipp (2009) Geographic intention and modification in Web search. International Journal of Geographical Information Science, 22(3). Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. A. Lacerda, M. Cristo, M. A. Gonçalves, W. Fan, N. Ziviani, and B. Ribeiro-Neto (2006) Learning to advertise. In Proceedings of the 29th ACM SIGIR Conference on Research and Development in information Retrieval. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. L. Leidner (2007). Toponym Resolution: a Comparison and Taxonomy of Heuristics and Methods. PhD Thesis, University of Edinburgh.Google ScholarGoogle Scholar
  11. H. Lin, C. Lin, and R. Weng (2007) A note on Platt's probabilistic outputs for support vector machines, Machine Learning, 68(3). Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. A. Markowetz, Y. Y. Chen, T. Suel, X. Long, and B. Seeger (2005) Design and implementation of a geographic search engine. In Proceedings of the 8th International Workshop on the Web and Databases.Google ScholarGoogle Scholar
  13. B. Martins, I. Anastácio, P. Calado (2010) A Machine Learning Approach for Resolving Place References in Text. In Proceedings of the 13th AGILE International Conference on Geographic Information Science. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. B. Martins, N. Cardoso, M. S. Chaves, L. Andrade, and M. J. Silva (2007) The University of Lisbon at GeoCLEF 2006. Evaluation of Multilingual and Multi-modal Information Retrieval. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. B. Martins, and M. J. Silva, (2005) A Graph-Ranking Algorithm for Geo-Referencing Documents, In Proceedings of the 5th IEEE International Conference on Data Mining. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. B. Ribeiro-Neto, M. Cristo, P. B. Golgher, and E. Silva de Moura (2005) Impedance coupling in content-targeted advertising. In Proceedings of the 28th ACM SIGIR Conference on Research and Development in information Retrieval. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. F. Sebastiani (2002) Machine learning in automated text categorization. ACM Computing Surveys, 34(1). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. D. A. Smith, and G. Crane (2001) Disambiguating Geographic Names in a Historical Digital Library. In Proceedings of the 5th European Conference on Research and Advanced Technology For Digital Libraries. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. C. Wang, P. Zhang, R. Choi, and M. D. Eredita (2002) Understanding consumers attitude toward advertising. In Proceedings of the 8th Americas Conference on Information Systems.Google ScholarGoogle Scholar
  20. I. H. Witten, and E. Frank (2000) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. A. G. Woodruff, and C. Plaunt (1994) GIPSY: automated geographic indexing of text documents. Journal of the American Society of Information Sciences, 45(9). Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Y. Wu, E. Y. Chang, K. C. Chang, and J. R. Smith (2004) Optimal multimodal fusion for multimedia data analysis. In Proceedings of the 12th annual ACM international conference on Multimedia. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. W. Yih, J. Goodman, and V. R. Carvalho (2006) Finding advertising keywords on web pages. In Proceedings of the 15th international conference on World Wide Web. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Other conferences
        GIR '10: Proceedings of the 6th Workshop on Geographic Information Retrieval
        February 2010
        130 pages
        ISBN:9781605588261
        DOI:10.1145/1722080

        Copyright © 2010 ACM

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        New York, NY, United States

        Publication History

        • Published: 18 February 2010

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