skip to main content
10.1145/1571941.1572002acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
research-article

Click-through prediction for news queries

Published:19 July 2009Publication History

ABSTRACT

A growing trend in commercial search engines is the display of specialized content such as news, products, etc. interleaved with web search results. Ideally, this content should be displayed only when it is highly relevant to the search query, as it competes for space with "regular" results and advertisements. One measure of the relevance to the search query is the click-through rate the specialized content achieves when displayed; hence, if we can predict this click-through rate accurately, we can use this as the basis for selecting when to show specialized content. In this paper, we consider the problem of estimating the click-through rate for dedicated news search results. For queries for which news results have been displayed repeatedly before, the click-through rate can be tracked online; however, the key challenge for which previously unseen queries to display news results remains. In this paper we propose a supervised model that offers accurate prediction of news click-through rates and satisfies the requirement of adapting quickly to emerging news events.

References

  1. D. Agarwal, B. Chen, P. Elango, R. Ramakrishnan, N. Motgi, S. Roy, and J. Zachariah. Online Models for Content Optimization. In NIPS, 2008.Google ScholarGoogle Scholar
  2. E. Agichtein, E. Brill, and S. Dumais. Improving Web Search Ranking by Incorporating User Behavior Information. In ACM SIGIR, pages 19--26, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. Allan, R. Papka, and V. Lavrenko. On-line New Event Detection and Tracking. In ACM SIGIR, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to Rank using Gradient Descent. In ICML, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Callan. Distributed Information Retrieval. In Advances in Information Retrieval, pages 127--150, 2000.Google ScholarGoogle Scholar
  6. D. Carmel, E. Yom-Tov, A. Darlow, and D. Pelleg. What makes a Query Difficult? In ACM SIGIR, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. D. Chakrabarti, D. Agarwal, and V. Josifovski. Contextual Advertising by Combining Relevance with Click Feedback. In WWW Conference, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. G. M. D. Corso, A. Gull´1, and F. Romani. Ranking a Stream of News. In Proceedings of the 14th international conference on World Wide Web, pages 97--106, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. F. Diaz. Integration of News Content into Web Results. In WSDM Conference, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. Friedman. Greedy Function Approximation: a Gradient Boosting Machine. Annals of Statistics, 29(5), 2001.Google ScholarGoogle Scholar
  11. T. Jayram, S. Khot, R. Kumar, and Y. Rabani. Cell-Probe Lower Bounds for the Partial Match Problem. In STOC, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. K. S. Jones, S. Walker, and S. E. Robertson. A probabilistic model of information retrieval: Development and comparative experiments. Information Processing and Management, 36(6), 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. C. König, K. Church, and M. Markov. A Data Struture for Sponsored Search. In IEEE ICDE Conference, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. P. Li and K. W. Church. Using Sketches to Estimate Two-way and Multi-way Associations. Computational Linguistics, 33, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. X. Li, Y.-Y. Wang, and A. Acero. Learning Query Intent from Regularized Click Graphs. In In Proc. ACM of SIGIR, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. Lin. Divergence Measures based on the Shannon Entropy. IEEE Trans. on Information Theory, 37(1), 1991.Google ScholarGoogle Scholar
  17. C. Manning and H. Sch¨utze. Foundations of Statistical Natural Language Processing. MIT Press, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. A. Maykov and M. Hurst. Social Streams Blog Crawler. In M3SN Workshop at IEEE ICDE Conference, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. G. Navarro and V. M¨akinen. Compressed full-text indexes. ACM Comput. Surv., 39(1), 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. F. Radlinski and T. Joachims. Active Exploration for Learning Rankings from Clickthrough Data. In ACM SIKDD, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. M. Regelson and D. Fain. Predicting Click-through Rate using Keyword Clusters. In 2nd Workshop on Sponsored Search Auctions, 2006.Google ScholarGoogle Scholar
  22. M. Richardson, E. Dominowska, and R. Ragno. Predicting Clicks: Estimating the Click-Through Rate for New Ads. In WWW Conference, pages 521--529, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Technorati. State of the Live Web. http://technorati.com/weblog/2007/04/328.html, April 2007.Google ScholarGoogle Scholar
  24. Technorati. State of the Blogosphere 2008. http://technorati.com/blogging/state-of-the-blogosphere/, 2008.Google ScholarGoogle Scholar
  25. M. West and J. Harrison. Bayesian Forecasting and Dynamic Models. Springer-Verlag, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Q. Wu, C. J. Burges, K. M. Svore, and J. Gao. Ranking, Boosting, and Model Adaptation. Technical report, Microsoft Research, 2008Google ScholarGoogle Scholar

Index Terms

  1. Click-through prediction for news queries

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
        July 2009
        896 pages
        ISBN:9781605584836
        DOI:10.1145/1571941

        Copyright © 2009 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 19 July 2009

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate792of3,983submissions,20%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader