Using Intent Information to Model User Behavior in Diversified Search

  • Aleksandr Chuklin
  • Pavel Serdyukov
  • Maarten de Rijke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7814)


A result page of a modern commercial search engine often contains documents of different types targeted to satisfy different user intents (news, blogs, multimedia). When evaluating system performance and making design decisions we need to better understand user behavior on such result pages. To address this problem various click models have previously been proposed. In this paper we focus on result pages containing fresh results and propose a way to model user intent distribution and bias due to different document presentation types. To the best of our knowledge this is the first work that successfully uses intent and layout information to improve existing click models.


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  1. 1.
    Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM, p. 5. ACM (2009)Google Scholar
  2. 2.
    Arguello, J., Diaz, F., Callan, J., Crespo, J.: Sources of evidence for vertical selection. In: SIGIR, pp. 315–322. ACM (2009)Google Scholar
  3. 3.
    Arguello, J., Diaz, F., Callan, J.: Learning to Aggregate Vertical Results into Web Search Results. In: CIKM. ACM (2011)Google Scholar
  4. 4.
    Arguello, J., Diaz, F., Callan, J., Carterette, B.: A Methodology for Evaluating Aggregated Search Results. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 141–152. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Arguello, J., Diaz, F., Paiement, J.: Vertical selection in the presence of unlabeled verticals. In: SIGIR. ACM (2010)Google Scholar
  6. 6.
    Chapelle, O., Metzler, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: CIKM, p. 621. ACM (2009)Google Scholar
  7. 7.
    Chapelle, O., Zhang, Y.: A dynamic bayesian network click model for web search ranking. In: WWW. ACM (2009)Google Scholar
  8. 8.
    Chen, D., Chen, W., Wang, H.: Beyond ten blue links: enabling user click modeling in federated web search. In: WSDM. ACM (2012)Google Scholar
  9. 9.
    Clarke, C.L.A., Craswell, N., Soboroff, I.: A comparative analysis of cascade measures for novelty and diversity. In: WSDM, pp. 75–84. ACM (2011)Google Scholar
  10. 10.
    Clarke, C., Kolla, M., Cormack, G.: Novelty and diversity in information retrieval evaluation. In: SIGIR. ACM (2008)Google Scholar
  11. 11.
    Craswell, N., Zoeter, O., Taylor, M., Ramsey, B.: An experimental comparison of click position-bias models. In: WSDM, p. 87. ACM (2008)Google Scholar
  12. 12.
    Dumais, S., Cutrell, E., Chen, H.: Optimizing search by showing results in context. In: CHI. ACM (2001)Google Scholar
  13. 13.
    Dupret, G., Piwowarski, B.: A user browsing model to predict search engine click data from past observations. In: SIGIR 2008, pp. 331–338. ACM (2008)Google Scholar
  14. 14.
    Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap(Chapman & Hall/CRC Monographs on Statistics & Applied Probability), 1st edn. Chapman and Hall/CRC (May 1994)Google Scholar
  15. 15.
    Guo, F., Liu, C., Wang, Y.: Efficient multiple-click models in web search. In: WSDM. ACM (2009)Google Scholar
  16. 16.
    Guo, F., Liu, C., Kannan, A., Minka, T., Taylor, M., Wang, Y.M., Faloutsos, C.: Click chain model in web search. In: WWW. p. 11. ACM (2009)Google Scholar
  17. 17.
    Hu, B., Zhang, Y., Chen, W., Wang, G., Yang, Q.: Characterizing search intent diversity into click models. In: WWW, p. 17. ACM (2011)Google Scholar
  18. 18.
    Joachims, T., Granka, L., Pan, B., Hembrooke, H., Gay, G.: Accurately interpreting clickthrough data as implicit feedback. In: SIGIR, p. 154. ACM (2005)Google Scholar
  19. 19.
    Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (August 2009)Google Scholar
  20. 20.
    Liu, C., Guo, F., Faloutsos, C.: BBM. In: KDD, p. 537. ACM (June 2009)Google Scholar
  21. 21.
    Moffat, A., Zobel, J.: Rank-biased precision for measurement of retrieval effectiveness. ACM Transactions on Information Systems 27(1), 1–27 (2008)CrossRefGoogle Scholar
  22. 22.
    Ponnuswami, A.K., Pattabiraman, K., Wu, Q., Gilad-Bachrach, R., Kanungo, T.: On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals. In: WSDM. ACM (2011)Google Scholar
  23. 23.
    Radlinski, F., Dumais, S.: Improving personalized web search using result diversification. In: SIGIR. ACM (2006)Google Scholar
  24. 24.
    Sakai, T., Song, R.: Evaluating diversified search results using per-intent graded relevance. In: SIGIR. ACM (2011)Google Scholar
  25. 25.
    Styskin, A., Romanenko, F., Vorobyev, F., Serdyukov, P.: Recency ranking by diversification of result set. In: CIKM, pp. 1949–1952. ACM (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Aleksandr Chuklin
    • 1
    • 2
  • Pavel Serdyukov
    • 1
  • Maarten de Rijke
    • 2
  1. 1.YandexMoscowRussia
  2. 2.ISLAUniversity of AmsterdamThe Netherlands

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