Applying Contextual Advertising to MultiModal Information Content

  • Giuliano Armano
  • Alessandro Giuliani
  • Alberto Messina
  • Maurizio Montagnuolo
  • Eloisa Vargiu
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 123)

Abstract

Contextual Advertising, a major sources of income for a large number of websites, is aimed at suggesting products and services to the ever growing population of Internet users. In this paper, we focus on the problem of suggesting suitable advertisements to news aggregation from television and from the Internet. To our best knowledge, this is the first attempt to perform this task in the field of multimodal aggregation. The proposed system suggests from 1 to 5 advertisements related to the main topic of aggregated news items. 15 users were asked to evaluate the relevance of the suggested advertisements. Preliminary results are encouraging for further development and application of contextual advertising in the field of multimodal aggregation.

Keywords

News Story Text Summarization Religious Culture Contextual Advertising Large Customer Base 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Feldman, J., Muthukrishnan, S.: Algorithmic methods for sponsored search advertising. CoRR abs/0805.1759 (2008)Google Scholar
  2. 2.
    Broder, A., Fontoura, M., Josifovski, V., Riedel, L.: A semantic approach to contextual advertising. In: SIGIR 2007: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 559–566. ACM, New York (2007)CrossRefGoogle Scholar
  3. 3.
    Anagnostopoulos, A., Broder, A.Z., Gabrilovich, E., Josifovski, V., Riedel, L.: Just-in-time contextual advertising. In: CIKM 2007: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 331–340. ACM, New York (2007)CrossRefGoogle Scholar
  4. 4.
    Armano, G., Giuliani, A., Vargiu, E.: Studying the impact of text summarization on contextual advertising. In: 8th International Workshop on Text-based Information Retrieval (2011)Google Scholar
  5. 5.
    Messina, A., Montagnuolo, M.: Multimodal Aggregation and Recommendation Technologies Applied to Informative Content Distribution and Retrieval. In: Soro, A., Vargiu, E., Armano, G., Paddeu, G. (eds.) Information Retrieval and Mining in Distributed Environments. SCI, vol. 324, pp. 213–232. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill Book Company (1984)Google Scholar
  7. 7.
    Rocchio, J.: Relevance feedback in information retrieval. In: The SMART Retrieval System: Experiments in Automatic Document Processing, pp. 313–323. Prentice Hall (1971)Google Scholar
  8. 8.
    Armano, G., Giuliani, A., Vargiu, E.: Experimenting text summarization techniques for contextual advertising. In: IIR 2011: Proceedings of the 2nd Italian Information Retrieval (IIR) Workshop (2011)Google Scholar
  9. 9.
    Armano, G., Giuliani, A., Messina, A., Montagnuolo, M., Vargiu, E.: Experimenting text summarization on multimodal aggregation. In: Lai, C., Semeraro, G., Vargiu, E. (eds.) 5th International Workshop DART 2011, New Challenges on Information Retrieval and Filtering. CEUR Workshop Proceedings, vol. 771 (2011)Google Scholar
  10. 10.
    Ciaramita, M., Murdock, V., Plachouras, V.: Semantic associations for contextual advertising. Journal of Electronic Commerce Research 9(1), 1–15 (2008); Special Issue on Online Advertising and Sponsored SearchGoogle Scholar
  11. 11.
    Järvelin, K., Kekäläinen, J.: Ir evaluation methods for retrieving highly relevant documents. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2000, pp. 41–48. ACM, New York (2000)CrossRefGoogle Scholar
  12. 12.
    Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 621–630. ACM, New York (2009)CrossRefGoogle Scholar
  13. 13.
    Amer-Yahia, S., Roy, S.B., Chawlat, A., Das, G., Yu, C.: Group recommendation: semantics and efficiency. Proc. VLDB Endow. 2(1), 754–765 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Giuliano Armano
    • 1
  • Alessandro Giuliani
    • 1
  • Alberto Messina
    • 2
  • Maurizio Montagnuolo
    • 2
  • Eloisa Vargiu
    • 1
    • 3
  1. 1.Dept. of Electrical and Electronic EngineeringUniversity of CagliariCagliariItaly
  2. 2.RAI Centre for Research and Technological InnovationTorinoItaly
  3. 3.Barcelona DigitalBarcelonaSpain

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