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The impact of history length on personalized search

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SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

Pages 715 - 716

https://doi.org/10.1145/1390334.1390466

Published: 20 July 2008 Publication History

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Abstract

Personalized search is a promising way to better serve different users' information needs. Search history is one of the major information sources for search personalization. We investigated the impact of history length on the effectiveness of personalized ranking. We carried out task-based user study for Web search, and obtained ranked relevance judgments for all queries. Query contexts derived from previous queries in the same task are used to re-rank results for the current query. Experimental results show that the performance of personalization generally improves as more queries are accumulated, but most of the benefits come from a few immediately preceding queries.

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Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Jayanthi JSelvakumar J(2017)A novel framework to facilitate personalized web search in a dual modeCluster Computing10.1007/s10586-017-1128-520:4(3527-3535)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s10586-017-1128-5
Shekarpour SNgonga Ngomo AAuer S(2013)Keyword-Driven Resource Disambiguation over RDF Knowledge BasesSemantic Technology10.1007/978-3-642-37996-3_11(159-174)Online publication date: 2013
https://doi.org/10.1007/978-3-642-37996-3_11
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The impact of history length on personalized search
1. Information systems
  1. Information retrieval

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Published In

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

July 2008

934 pages

ISBN:9781605581644

DOI:10.1145/1390334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Mun-Kew Leong
National Library Board, Singapore
,
Program Chairs:
Syung Hyon Myaeng
Information and Communications University, Korea
,
Douglas W. Oard
University of Maryland, College Park, USA
,
Fabrizio Sebastiani
Consiglio Nazionale delle Ricerche, Italy

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Association for Computing Machinery

New York, NY, United States

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Published: 20 July 2008

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SIGIR '08: The 31st Annual International ACM SIGIR Conference

July 20 - 24, 2008

Singapore, Singapore

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Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Jayanthi JSelvakumar J(2017)A novel framework to facilitate personalized web search in a dual modeCluster Computing10.1007/s10586-017-1128-520:4(3527-3535)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s10586-017-1128-5
Shekarpour SNgonga Ngomo AAuer S(2013)Keyword-Driven Resource Disambiguation over RDF Knowledge BasesSemantic Technology10.1007/978-3-642-37996-3_11(159-174)Online publication date: 2013
https://doi.org/10.1007/978-3-642-37996-3_11
Anastasiu DGao BJiang XKarypis G(2011)A novel two-box search paradigm for query disambiguationWorld Wide Web10.1007/s11280-011-0154-016:1(1-29)Online publication date: 28-Dec-2011
https://doi.org/10.1007/s11280-011-0154-0
Gao BAnastasiu DJiang XJoshi AHuang CJurafsky D(2010)Utilizing user-input contextual terms for query disambiguationProceedings of the 23rd International Conference on Computational Linguistics: Posters10.5555/1944566.1944604(329-337)Online publication date: 23-Aug-2010
https://dl.acm.org/doi/10.5555/1944566.1944604
Kim HLee SLee BKang S(2010)Building Concept Network-Based User Profile for Personalized Web SearchProceedings of the 2010 IEEE/ACIS 9th International Conference on Computer and Information Science10.1109/ICIS.2010.56(567-572)Online publication date: 18-Aug-2010
https://dl.acm.org/doi/10.1109/ICIS.2010.56

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Automatic identification of user interest for personalized search

Search personalization through query and page topical analysis

Personalized Search: Potential and Pitfalls

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