research-article

Search Engines that Learn from Implicit Feedback

Authors:

Thorsten Joachims,

Filip RadlinskiAuthors Info & Claims

Computer, Volume 40, Issue 8

Pages 34 - 40

https://doi.org/10.1109/MC.2007.289

Published: 01 August 2007 Publication History

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Abstract

Search-engine logs provide a wealth of information that machine-learning techniques can harness to improve search quality. With proper interpretations that avoid inherent biases, a search engine can use training data extracted from the logs to automatically tailor ranking functions to a particular user group or collection.

References

[1]

J. Teevan, S.T. Dumais, and E. Horvitz, "Characterizing the Value of Personalizing Search," to be published in Proc. ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 07), ACM Press, 2007.

Digital Library

Google Scholar

[2]

F. Radlinski and T. Joachims, "Query Chains: Learning to Rank from Implicit Feedback," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 05), ACM Press, 2005, pp. 239–248.

Digital Library

Google Scholar

[3]

T. Joachims et al., "Evaluating the Accuracy of Implicit Feedback from Clicks and Query Reformulations in Web Search," ACM Trans. Information Systems, vol. 25, no. 2, article 7, 2007.

Digital Library

Google Scholar

[4]

F. Radlinski and T. Joachims, "Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs," Proc. Nat'l Conf. Am. Assoc. for Artificial Intelligence (AAAI 06), AAAI, 2006, pp. 1406–1412.

Digital Library

Google Scholar

[5]

F. Radlinski and T. Joachims, "Active Exploration for Learning Rankings from Clickthrough Data," to be published in Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 07), ACM Press, 2007.

Digital Library

Google Scholar

[6]

T. Joachims, "Optimizing Search Engines Using Clickthrough Data," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 02), ACM Press, 2002, pp. 132–142.

Digital Library

Google Scholar

[7]

D. Kelly and J. Teevan, "Implicit Feedback for Inferring User Preference: A Bibliography," ACM SIGIR Forum, vol. 37, no. 2, 2003, pp. 18–28.

Digital Library

Google Scholar

[8]

E. Agichtein, E. Brill, and S. Dumais, "Improving Web Search Ranking by Incorporating User Behavior," Proc. ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 06), ACM Press, 2006, pp. 19–26.

Digital Library

Google Scholar

[9]

G. Furnas, "Experience with an Adaptive Indexing Scheme," Proc. ACM SIGCHI Conf. Human Factors in Computing Systems (CHI 85), ACM Press, 1985, pp. 131–135.

Digital Library

Google Scholar

[10]

W.W. Cohen, R.E. Shapire, and Y. Singer, "Learning to Order Things," J. Artificial Intelligence Research, vol. 10, AI Access Foundation, Jan.–June 1999, pp. 243–270.

Digital Library

Google Scholar

[11]

V. Vapnik, Statistical Learning Theory, John Wiley & Sons, 1998.

Google Scholar

[12]

R. Herbrich, T. Graepel, and K. Obermayer, "Large-Margin Rank Boundaries for Ordinal Regression," P. Bartlett et al., eds., Advances in Large-Margin Classifiers, MIT Press, 2000, pp. 115–132.

Google Scholar

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Search Engines that Learn from Implicit Feedback

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Published In

Computer Volume 40, Issue 8

August 2007

87 pages

ISSN:0018-9162

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IEEE Computer Society Press

Washington, DC, United States

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Published: 01 August 2007

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Liu XGuo CYao BSarikaya R(2024)A Self-Learning Framework for Large-Scale Conversational AI SystemsIEEE Computational Intelligence Magazine10.1109/MCI.2024.336397119:2(34-48)Online publication date: 5-Apr-2024
https://dl.acm.org/doi/10.1109/MCI.2024.3363971
Karamiyan FMahootchi MMohebi A(2024)A personalized ranking method based on inverse reinforcement learning in search enginesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108915136:PAOnline publication date: 1-Oct-2024
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Abstract

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Cited By

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Overlap Among Major Web Search Engines

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