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Exploring relevance for clicks

Published: 02 November 2009 Publication History

Abstract

Mining feedback information from user click-through data is an important issue for modern Web retrieval systems in terms of architecture analysis, performance evaluation and algorithm optimization. For commercial search engines, user click-through data contains useful information as well as large amount of inevitable noises. This paper proposes an approach to recognize reliable and meaningful user clicks (referred to as Relevant Clicks, RCs) in click-through data. By modeling user click-through behavior on search result lists, we propose several features to separate RCs from click noises. A learning algorithm is presented to estimate the quality of user clicks. Experimental results on large scale dataset show that: 1) our model effectively identifies RCs in noisy click-through data; 2) Different from previous click-through analysis efforts, our approach works well for both hot queries and long-tail queries.

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Cited By

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  • (2022)Noise-Reduction for Automatically Transferred Relevance JudgmentsExperimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-031-13643-6_4(48-61)Online publication date: 25-Aug-2022
  • (2014)Randomized algorithm for Information Retrieval using past search results2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2014.6861068(1-9)Online publication date: May-2014
  • (2013)An approach for web page ordering using user session2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES10.1109/CICT.2013.6558245(1009-1013)Online publication date: Apr-2013
  • Show More Cited By

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cover image ACM Conferences
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management
November 2009
2162 pages
ISBN:9781605585123
DOI:10.1145/1645953
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

New York, NY, United States

Publication History

Published: 02 November 2009

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Author Tags

  1. click relevance
  2. click-through data
  3. implicit feedback
  4. long-tail query

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2022)Noise-Reduction for Automatically Transferred Relevance JudgmentsExperimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-031-13643-6_4(48-61)Online publication date: 25-Aug-2022
  • (2014)Randomized algorithm for Information Retrieval using past search results2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2014.6861068(1-9)Online publication date: May-2014
  • (2013)An approach for web page ordering using user session2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES10.1109/CICT.2013.6558245(1009-1013)Online publication date: Apr-2013
  • (2013)A comparative analysis of clickstream as web page importance metric2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES10.1109/CICT.2013.6558199(776-781)Online publication date: Apr-2013

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