Multiple Clicks Model for Web Search of Multi-clickable Documents

Léa Laporte, Sébastien Déjean, Josiane Mothe

Abstract

This paper presents a novel document relevance model based on clickthrough information. Compared to the models from the literature we consider the case when documents can be clicked several times in a given search session. This case occurs more and more frequently, specifically for multi-clickable documents such as maps in location search enginess. Considering a real system query log, we evaluate our model and show that SVM can learn with fewer errors and with better MAP when the various types of clicks are considered in the model.

References

  1. Borlund, P. (2003). The iir evaluation model: a framework for evaluation of interactive information retrieval systems. Information research, 8(3):8-3.
  2. Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., and Hullender, G. (2005). Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning, ICML 7805, pages 89-96.
  3. Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., and Li, H. (2007). Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning, ICML 7807, pages 129-136.
  4. Chapelle, O. and Zhang, Y. (2009). A dynamic bayesian network click model for web search ranking. In Proceedings of 18th International Conference on World Wide Web, WWW'09, pages 1-10.
  5. Cleverdon, C. W., Mills, J., and Keen, M. (1966). Factors determining the performance of indexing systems.
  6. Cossock, D. and Zhang, T. (2006). Subset ranking using regression. In Proceedings of the 19th annual conference on Learning Theory, COLT'06, pages 605-619. Springer-Verlag.
  7. Craswell, N., Zoeter, O., Taylor, M., and Ramsey, B. (2008). An experimental comparison of click position-bias models. In Proceedings of the 2008 International Conference on Web Search and Data Mining, pages 87-94.
  8. Dupret, G., Murdock, V., and Piwowarski, B. (2007). Web search engine evaluation using clickthrough data and a user model. In WWW2007 workshop Query Log Analysis: Social and Technological Challenges.
  9. Freund, Y., Iyer, R., Schapire, R. E., and Singer, Y. (2003). An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4:933- 969.
  10. Guo, F., Liu, C., and Wang, Y. M. (2009). Efficient multiple-click models in web search. In Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM 7809, pages 124-131.
  11. Harman, D. (2010). Is the cranfield paradigm outdated. In Proc. 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, page 1.
  12. He, J., Zhao, W. X., Shu, B., Li, X., and Yan, H. (2011). Efficiently collecting relevance information from clickthroughs for web retrieval system evaluation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, SIGIR 7811, pages 275-284.
  13. Joachims, T. (2002). Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 7802, pages 133-142.
  14. Joachims, T., Granka, L., Pan, B., Hembrooke, H., and Gay, G. (2005). Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR 7805, pages 154-161.
  15. Laporte, L., Candillier, L., Déjean, S., and Mothe, J. (2012). Ó valuation de la pertinence dans les moteurs de recherche géoréférencés. In Actes du 30ème Congrès INFORSID, pages 281-298.
  16. Liu, C., Guo, F., and Faloutsos, C. (2010). Bayesian browsing model: Exact inference of document relevance from petabyte-scale data. ACM Transaction on Knowledge Discovery Data, 4(4):19:1-19:26.
  17. Liu, T.-Y. (2011). Learning to rank for information retrieval. Springer.
  18. Nallapati, R. (2004). Discriminative models for information retrieval. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR 7804, pages 64-71.
  19. Radlinski, F. and Joachims, T. (2005). Query chains: learning to rank from implicit feedback. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, KDD 7805, pages 239-248.
  20. Yue, Y., Finley, T., Radlinski, F., and Joachims, T. (2007). A support vector method for optimizing average precision. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR 7807, pages 271-278.
  21. Yue, Y., Gao, Y., Chapelle, O., Zhang, Y., and Joachims, T. (2010). Learning more powerful test statistics for click-based retrieval evaluation. In Proceedings of the 33rd annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR'10, pages 507-514.
Download


Paper Citation


in Harvard Style

Laporte L., Déjean S. and Mothe J. (2013). Multiple Clicks Model for Web Search of Multi-clickable Documents . In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-59-4, pages 298-303. DOI: 10.5220/0004553902980303


in Bibtex Style

@conference{iceis13,
author={Léa Laporte and Sébastien Déjean and Josiane Mothe},
title={Multiple Clicks Model for Web Search of Multi-clickable Documents},
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2013},
pages={298-303},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004553902980303},
isbn={978-989-8565-59-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Multiple Clicks Model for Web Search of Multi-clickable Documents
SN - 978-989-8565-59-4
AU - Laporte L.
AU - Déjean S.
AU - Mothe J.
PY - 2013
SP - 298
EP - 303
DO - 10.5220/0004553902980303