Advertisement

Introducing the User-over-Ranking Hypothesis

  • Benno Stein
  • Matthias Hagen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6611)

Abstract

The User-over-Ranking hypothesis states that rather the user herself than a web search engine’s ranking algorithm can help to improve retrieval performance. The means are longer queries that provide additional keywords.

Readers who take this hypothesis for granted should recall the fact that virtually no user and none of the search index providers consider its implications. For readers who feel insecure about the claim, our paper gives empirical evidence.

Keywords

Retrieval Performance Keyword Query Result List Search Session Query Length 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Balasubramanian, N., Kumaran, G., Carvalho, V.R.: Exploring Reductions for Long Web Queries. In: Proceedings of SIGIR 2010, pp. 571–578 (2010)Google Scholar
  2. 2.
    Barker, K., Cornacchia, N.: Using Noun Phrase Heads to Extract Document Keyphrases. In: Proceedings of AI 2000, pp. 40–52 (2000)Google Scholar
  3. 3.
    Bendersky, M., Croft, W.B.: Discovering Key Concepts in Verbose Queries. In: Proceedings of SIGIR 2008, pp. 491–498 (2008)Google Scholar
  4. 4.
    Hagen, M., Stein, B.: Capacity-constrained Query Formulation. In: Proceedings of ECDL 2010, pp. 384–388 (2010)Google Scholar
  5. 5.
    Hagen, M., Potthast, M., Stein, B., Bräutigam, C.: The Power of Naïve Query Segmentation. In: Proceedings of SIGIR 2010, pp. 797–798 (2010)Google Scholar
  6. 6.
    Huston, S., Croft, W.B.: Evaluating Verbose Query Processing Techniques. In: Proceedings of SIGIR 2010, pp. 291–298 (2010)Google Scholar
  7. 7.
    Kumaran, G., Allan, J.: Adapting Information Retrieval Systems to User Queries. Information Processing and Management 44(6), 1838–1862 (2008)CrossRefGoogle Scholar
  8. 8.
    Lease, M., Allan, J., Croft, W.B.: Regression Rank: Learning to Meet the Opportunity of Descriptive Queries. In: Proceedings of ECIR 2009, pp. 90–101 (2009)Google Scholar
  9. 9.
    Pass, G., Chowdhury, A., Torgeson, C.: A Picture of Search. In: Proceedings of Infoscale 2006, article number: 1 (2006)Google Scholar
  10. 10.
    Stein, B., Hagen, M.: Making the Most of a Web Search Session. In: Proceedings of WI-IAT 2010, pp. 90–97 (2010)Google Scholar
  11. 11.
    Tunkelang, D.: Faceted Search. Morgan & Claypool Publishers (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Benno Stein
    • 1
  • Matthias Hagen
    • 1
  1. 1.Bauhaus-Universität WeimarGermany

Personalised recommendations