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Web Search Relevance Ranking

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Synonyms

Ranking; Result ranking; Search ranking

Definition

Web search engines return lists of web pages sorted by the page’s relevance to the user query. The problem with web search relevance ranking is to estimate relevance of a page to a query. Nowadays, commercial web-page search engines combine hundreds of features to estimate relevance. The specific features and their mode of combination are kept secret to fight spammers and competitors. Nevertheless, the main types of features at use, as well as the methods for their combination, are publicly known and are the subject of scientific investigation.

Historical Background

Information Retrieval (IR) Systems are the predecessors of Web and search engines. These systems were designed to retrieve documents in curated digital collections such as library abstracts, corporate documents, news, etc. Traditionally, IR relevance ranking algorithms were designed to obtain high recall on medium-sized document collections using long detailed...

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Recommended Reading

  1. Baeza-Yates R, Ribeiro-Neto B. Modern information retrieval. Reading: Addison Wesley; 1999.

    Google Scholar 

  2. Boyan J, Freitag D, Joachims T. A machine learning architecture for optimizing web search engines. In: Proceedings of the AAAI Workshop on Internet Based Information Systems; 1996.

    Google Scholar 

  3. Culliss G. The direct hit popularity engine technology. A white paper, DirectHit. 2000. Available online at https://www.uni-koblenz.de/FB4/Institutes/ICV/AGKrause/Teachings/SS07/DirectHit.pdf. Accessed on 27 Nov 2007.

  4. Hawking D, Craswell N. Very large scale retrieval and Web search. In: Voorhees E, Harman D, editors. TREC: experiment and evaluation in information retrieval. Cambridge, MA: MIT Press; 2005.

    Google Scholar 

  5. Joachims T, Radlinski F. Search engines that learn from implicit feedback. IEEE Comp. 2007;40(8):34–40.

    Article  Google Scholar 

  6. Kleinberg J. Authoritative sources in a hyperlinked environment. Technical Report RJ 10076, IBM. 1997.

    Google Scholar 

  7. Langville AN, Meyer CD. Google’s PageRank and beyond: the science of search engine rankings. Princeton: Princeton University Press; 2006.

    Book  MATH  Google Scholar 

  8. Manning CD, Raghavan P, Schütze H. Introduction to information retrieval. Cambridge, UK: Cambridge University Press; 2008.

    Book  MATH  Google Scholar 

  9. Marchiori M. The quest for correct information on the Web: hyper search engines. In: Proceedings of the 6th International World Wide Web Conference; 1997.

    Google Scholar 

  10. Najork M. Comparing the effectiveness of HITS and SALSA. In: Proceedings of the 16th ACM International Conference on Information and Knowledge Management; 2007. p. 157–64.

    Google Scholar 

  11. Page L, Brin S, Motwani R, Winograd T. The PageRank citation ranking: bringing order to the Web. Technical Report, Stanford Digital Library Technologies Project.

    Google Scholar 

  12. Richardson M, Prakash A, Brill E. Beyond PageRank: machine learning for static ranking. In: Proceedings of the 15th International World Wide Web Conference; 2006. p. 707–15.

    Google Scholar 

  13. Taylor M, Zaragoza H, Craswell N, Robertson S, Burges C. Optimisation methods for ranking functions with multiple parameters. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management; 2006. p. 585–93.

    Google Scholar 

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Correspondence to Hugo Zaragoza .

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Zaragoza, H., Najork, M. (2018). Web Search Relevance Ranking. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_463

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