Skip to main content
SpringerLink
Log in

Web Search Relevance Feedback

  • Reference work entry
Encyclopedia of Database Systems

Definition

Relevance feedback refers to an interactive cycle that helps to improve the retrieval performance based on the relevance judgments provided by a user. Specifically, when a user issues a query to describe an information need, an information retrieval system would first return a set of initial results and then ask the user to judge whether some information items (typically documents or passages) are relevant or not. After that, the system would reformulate the query based on the collected feedback information, and return a set of retrieval results, which presumably would be better than the initial retrieval results. This procedure could be repeated.

Historical Background

Quality of retrieval results highly depends on how effective a user’s query (usually a set of keywords) is in distinguishing relevant documents from non-relevant ones. Ideally, the keywords used in the query should occur only in the relevant documents and not in any non-relevant document. Unfortunately, in...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 2,500.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. Allan J. Relevance feedback with too much data. In Proc. 18th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 1995, pp. 337–343.

    Google Scholar 

  2. Buckley C. Automatic query expansion using SMART: TREC-3. In Overview of the Third Text Retrieval Conference (TREC-3), D. Harman (ed.), 1995, pp. 69–80.

    Google Scholar 

  3. Burges C., Shaked T., Renshaw E., Lazier A., Deeds M., Hamilton N., and Hullender G. Learning to rank using gradient descent. In Proc. 22nd Int. Conf. on Machine Learning, 2005, pp. 89–96.

    Google Scholar 

  4. Joachims T. Optimizing search engines using clickthrough data. In Proc. 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2002, pp. 133–142.

    Google Scholar 

  5. Kelly D. and Teevan J. Implicit feedback for inferring user preference. SIGIR Forum, 37(2):18–28, 2003.

    Article  Google Scholar 

  6. Robertson S.E. and Jones K.S. Relevance weighting of search terms. J. Am. Soc. Inf. Sci., 27(3):129–146, 1976.

    Article  Google Scholar 

  7. Robertson S.E., Walker S., Jones S., Hancock-Beaulieu M.M., and Gatford M. Okapi at TREC-3. In Proc. The 3rd Text Retrieval Conference, 1995, pp. 109–126.

    Google Scholar 

  8. Rocchio J. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice-Hall, Englewood Cliffs, NJ, 1971, pp. 313–323.

    Google Scholar 

  9. Ruthven I. and Lalmas M. A survey on the use of relevance feedback for information access system. Knowl. Eng. Rev., 18(2):95–145, 2003.

    Article  Google Scholar 

  10. Salton G. and Buckley C. Improving retrieval performance by relevance feedback. J. Am. Soc. Inf. Sci., 44(4):288–297, 1990.

    Article  Google Scholar 

  11. Shen X. and Zhai C. Active feedback in ad hoc information retrieval. In Proc. 31st Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2005, pp. 59–66.

    Google Scholar 

  12. Singhal A., Mitra M., and Buckley C. Learning routing queries in a query zone. In Proc. 20th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 1997, pp. 25–32.

    Google Scholar 

  13. Wang X., Fang H., and Zhai C. A study of methods for negative relevance feedback. In Proc. 34th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2008, pp. 219–226.

    Google Scholar 

  14. Xu J. and Croft W.B. Query expansion using local and global document analysis. In Proc. 19th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 1996, pp. 4–11.

    Google Scholar 

  15. Zhai C. and Lafferty J. Model-based feedback in the language modeling approach to information retrieval. In Proc. Int. Conf. on Information and Knowledge Management, 2001, pp. 403–410.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Delaware, Newark, DE, USA

    Hui Fang

  2. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    ChengXiang Zhai

Authors
  1. Hui Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ChengXiang Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Fang, H., Zhai, C. (2009). Web Search Relevance Feedback. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_462

Download citation

Publish with us

Policies and ethics

Search

Navigation