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Enhancing web search by promoting multiple search engine use

Published: 20 July 2008 Publication History

Abstract

Any given Web search engine may provide higher quality results than others for certain queries. Therefore, it is in users' best interest to utilize multiple search engines. In this paper, we propose and evaluate a framework that maximizes users' search effective-ness by directing them to the engine that yields the best results for the current query. In contrast to prior work on meta-search, we do not advocate for replacement of multiple engines with an aggregate one, but rather facilitate simultaneous use of individual engines. We describe a machine learning approach to supporting switching between search engines and demonstrate its viability at tolerable interruption levels. Our findings have implications for fluid competition between search engines.

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  • (2017)Characterizing, predicting, and handling web search queries that match very few or no resultsJournal of the Association for Information Science and Technology10.1002/asi.2395569:2(256-270)Online publication date: 22-Sep-2017
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cover image ACM Conferences
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
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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Published: 20 July 2008

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Cited By

View all
  • (2018)Skill discovery in virtual assistantsCommunications of the ACM10.1145/318533661:11(106-113)Online publication date: 26-Oct-2018
  • (2018)A selective approach to index term weighting for robust information retrieval based on the frequency distributions of query termsInformation Retrieval Journal10.1007/s10791-018-9347-922:6(543-569)Online publication date: 13-Dec-2018
  • (2017)Characterizing, predicting, and handling web search queries that match very few or no resultsJournal of the Association for Information Science and Technology10.1002/asi.2395569:2(256-270)Online publication date: 22-Sep-2017
  • (2016)Measuring and Predicting Search Engine Users’ SatisfactionACM Computing Surveys10.1145/289348649:1(1-35)Online publication date: 28-Jul-2016
  • (2015)Domain-independent search expertiseJournal of the Association for Information Science and Technology10.1002/asi.2327266:7(1388-1405)Online publication date: 1-Jul-2015
  • (2014)Multimedia search rerankingACM Computing Surveys10.1145/253679846:3(1-38)Online publication date: 1-Jan-2014
  • (2014)LoyalTracker: Visualizing Loyalty Dynamics in Search EnginesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2014.234691220:12(1733-1742)Online publication date: 31-Dec-2014
  • (2014)Trust-based improved recommendation of IT-related Web resources2014 XL Latin American Computing Conference (CLEI)10.1109/CLEI.2014.6965185(1-6)Online publication date: Sep-2014
  • (2014)Landscape specification resizing2014 XL Latin American Computing Conference (CLEI)10.1109/CLEI.2014.6965100(1-10)Online publication date: Sep-2014
  • (2013)Slow SearchProceedings of the Symposium on Human-Computer Interaction and Information Retrieval10.1145/2528394.2528395(1-10)Online publication date: 3-Oct-2013
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