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Classification-enhanced ranking

Published: 26 April 2010 Publication History

Abstract

Many have speculated that classifying web pages can improve a search engine's ranking of results. Intuitively results should be more relevant when they match the class of a query. We present a simple framework for classification-enhanced ranking that uses clicks in combination with the classification of web pages to derive a class distribution for the query. We then go on to define a variety of features that capture the match between the class distributions of a web page and a query, the ambiguity of a query, and the coverage of a retrieved result relative to a query's set of classes. Experimental results demonstrate that a ranker learned with these features significantly improves ranking over a competitive baseline. Furthermore, our methodology is agnostic with respect to the classification space and can be used to derive query classes for a variety of different taxonomies.

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cover image ACM Other conferences
WWW '10: Proceedings of the 19th international conference on World wide web
April 2010
1407 pages
ISBN:9781605587998
DOI:10.1145/1772690

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2010

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Author Tags

  1. learning to rank
  2. query classification

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WWW '10
WWW '10: The 19th International World Wide Web Conference
April 26 - 30, 2010
North Carolina, Raleigh, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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