Article

Mining long-term search history to improve search accuracy

Authors:

Bin Tan,

Xuehua Shen,

ChengXiang ZhaiAuthors Info & Claims

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 718 - 723

https://doi.org/10.1145/1150402.1150493

Published: 20 August 2006 Publication History

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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.

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Cited By

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Chatur PJain NTrivedi VDhoot AMuthiah S(2024)Google Search in India: Unveiling the Geo-Personalized WebProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632420(403-411)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632420
Wang SDou ZLiu JZhu QWen J(2023)Personalized and Diversified: Ranking Search Results in an Integrated WayACM Transactions on Information Systems10.1145/363198942:3(1-25)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1145/3631989
Choi DXiang AOzturk OShrestha DDrake BHaidarian HJaved FPark HFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Patient Clustering via Integrated Profiling of Clinical and Digital DataProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615262(3818-3822)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615262
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Index Terms

Mining long-term search history to improve search accuracy
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2006

986 pages

ISBN:1595933395

DOI:10.1145/1150402

Conference Chair:
Tina Eliassi-Rad
LLNL
,
General Chair:
Lyle Ungar
University of Pennsylvania
,
Program Chairs:
Mark Craven
University of Wisconsin
,
Dimitrios Gunopulos
University of California, Riverside

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 20 August 2006

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KDD06: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 20 - 23, 2006

PA, Philadelphia, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Chatur PJain NTrivedi VDhoot AMuthiah S(2024)Google Search in India: Unveiling the Geo-Personalized WebProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632420(403-411)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632420
Wang SDou ZLiu JZhu QWen J(2023)Personalized and Diversified: Ranking Search Results in an Integrated WayACM Transactions on Information Systems10.1145/363198942:3(1-25)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1145/3631989
Choi DXiang AOzturk OShrestha DDrake BHaidarian HJaved FPark HFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Patient Clustering via Integrated Profiling of Clinical and Digital DataProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615262(3818-3822)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615262
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A session-based search engine

Discovering search engine related queries using association rules

Identifying popular search goals behind search queries to improve web search ranking

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