Classifying and Characterizing Query Intent

  • Azin Ashkan
  • Charles L. A. Clarke
  • Eugene Agichtein
  • Qi Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5478)


Understanding the intent underlying users’ queries may help personalize search results and improve user satisfaction. In this paper, we develop a methodology for using ad clickthrough logs, query specific information, and the content of search engine result pages to study characteristics of query intents, specially commercial intent. The findings of our study suggest that ad clickthrough features, query features, and the content of search engine result pages are together effective in detecting query intent. We also study the effect of query type and the number of displayed ads on the average clickthrough rate. As a practical application of our work, we show that modeling query intent can improve the accuracy of predicting ad clickthrough for previously unseen queries.


Result Page Query Type Query Feature Query Intent Commercial Intent 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ashkan, A., Clarke, C., Agichtein, E., Guo, Q.: Characterizing query intent from sponsored search clickthrough data. In: Proceedings of the SIGIR Workshop on Informational Retrieval for Advertising, pp. 15–22 (2008)Google Scholar
  2. 2.
    Broder, A.: A taxonomy of Web search. ACM SIGIR Forum 36(2), 3–10 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Canu, S., Grandvalet, Y., Guigue, V., Rakotomamonjy, A.: SVM and kernel methods matlab toolbox. Perception Systems et Information, INSA de Rouen, Rouen, France (2005)Google Scholar
  4. 4.
    Dai, H., Zhao, L., Nie, Z., Wen, J., Wang, L., Li, Y.: Detecting Online Commercial Intention (OCI). In: Proceedings of the 15th International Conference on World Wide Web, pp. 829–837 (2006)Google Scholar
  5. 5.
    Debmbsczynski, K., Kotlowski, W., Weiss, D.: Predicting ads clickthrough rate with decision rules. In: Workshop on Target and Ranking for Online Advertising, WWW 2008 (2008)Google Scholar
  6. 6.
    Jansen, B.: The comparative effectiveness of sponsored and nonsponsored links for Web e-commerce queries. ACM Transactions on the Web 1(1) (2007)Google Scholar
  7. 7.
    Jansen, B., Brown, A., Resnick, M.: Factors relating to the decision to click on a sponsored link. Decision Support Systems 44(1), 46–59 (2007)CrossRefGoogle Scholar
  8. 8.
    Lee, U., Liu, Z., Cho, J.: Automatic identification of user goals in Web search. In: Proceedings of the 14th International Conference on World Wide Web, pp. 391–400 (2005)Google Scholar
  9. 9.
    Regelson, M., Fain, D.: Predicting clickthrough rate using keyword clusters. In: Proceedings of the 2nd Workshop on Sponsored Search Auctions (2006)Google Scholar
  10. 10.
    Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the clickthrough rate for new ads. In: Proceedings of the 16th International Conference on World Wide Web, pp. 521–530 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Azin Ashkan
    • 1
  • Charles L. A. Clarke
    • 1
  • Eugene Agichtein
    • 2
  • Qi Guo
    • 2
  1. 1.University of WaterlooCanada
  2. 2.Emory UniversityUnited States

Personalised recommendations