Learning to rank user intent

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

  • Giorgos Giannopoulos
  • Ulf Brefeld
  • Theodore Dalamagas
  • Timos Sellis

Personalized retrieval models aim at capturing user interests to provide personalized results that are tailored to the respective information needs. User interests are however widely spread, subject to change, and cannot always be captured well, thus rendering the deployment of personalized models challenging. We take a different approach and study ranking models for user intent. We exploit user feedback in terms of click data to cluster ranking models for historic queries according to user behavior and intent. Each cluster is finally represented by a single ranking model that captures the contained search interests expressed by users. Once new queries are issued, these are mapped to the clustering and the retrieval process diversifies possible intents by combining relevant ranking functions. Empirical evidence shows that our approach significantly outperforms baseline approaches on a large corporate query log.

Original languageEnglish
Title of host publicationProceedings of the 20th ACM international conference on Information and knowledge management
Number of pages6
PublisherAssociation for Computing Machinery, Inc
Publication date2011
Pages195-200
ISBN (print)978-1-4503-0717-8
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event20th ACM Conference on Information and Knowledge Management - CIKM '11 - Glasgow, United Kingdom
Duration: 24.10.201128.10.2011
http://www.cikm2011.org/

DOI

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