Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Classification-enhanced Ranking

Paul N. Bennett, Krysta M. Svore, and Susan T. Dumais


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.


Publication typeInproceedings
Published inProceedings of World Wide Web
PublisherAssociation for Computing Machinery, Inc.
> Publications > Classification-enhanced Ranking