Mining long-term search history to improve search accuracy

Bin Tan; Xuehua Shen; Cheng Xiang Zhai

doi:10.1145/1150402.1150493

Mining long-term search history to improve search accuracy

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	KDD 2006
Subtitle of host publication	Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery (ACM)
Pages	718-723
Number of pages	6
ISBN (Print)	1595933395, 9781595933393
DOIs	https://doi.org/10.1145/1150402.1150493
State	Published - 2006
Event	KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Philadelphia, PA, United States Duration: Aug 20 2006 → Aug 23 2006

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume	2006

Other

Other	KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country	United States
City	Philadelphia, PA
Period	8/20/06 → 8/23/06

Keywords

Context
Query expansion
Search history

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/1150402.1150493

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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

Mining long-term search history to improve search accuracy. / Tan, Bin; Shen, Xuehua; Zhai, Cheng Xiang.

KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), 2006. p. 718-723 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tan, B, Shen, X & Zhai, CX 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. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 2006, Association for Computing Machinery (ACM), pp. 718-723, KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, United States, 8/20/06. https://doi.org/10.1145/1150402.1150493

Tan B, Shen X, Zhai CX. 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. Association for Computing Machinery (ACM). 2006. p. 718-723. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/1150402.1150493

Tan, Bin ; Shen, Xuehua ; Zhai, Cheng Xiang. / Mining long-term search history to improve search accuracy. KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), 2006. pp. 718-723 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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