Mining long-term search history to improve search accuracy

Bin Tan, Xuehua Shen, Cheng Xiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Long-term search history contains rich information about a user's search preferences, which can be used as search context to improve retrieval performance. In this paper, we study statistical language modeling based methods to mine contextual information from long-term search history and exploit it for a more accurate estimate of the query language model. Experiments on real web search data show that the algorithms are effective in improving search accuracy for both fresh and recurring queries. The best performance is achieved when using clickthrough data of past searches that are related to the current query.

Original languageEnglish (US)
Title of host publicationKDD 2006
Subtitle of host publicationProceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery (ACM)
Pages718-723
Number of pages6
ISBN (Print)1595933395, 9781595933393
DOIs
StatePublished - 2006
EventKDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Philadelphia, PA, United States
Duration: Aug 20 2006Aug 23 2006

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume2006

Other

OtherKDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CityPhiladelphia, PA
Period8/20/068/23/06

Keywords

  • Context
  • Query expansion
  • Search history

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Tan, B., Shen, X., & Zhai, C. X. (2006). Mining long-term search history to improve search accuracy. In KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 718-723). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2006). Association for Computing Machinery (ACM). https://doi.org/10.1145/1150402.1150493