Search Engines that Learn from Implicit Feedback

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http://doi.ieeecomputersociety.org/10.1109/MC.2007.289

August 2007 (vol. 40 no. 8) pp. 34-40

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	Thorsten Joachims, Filip Radlinski, "Search Engines that Learn from Implicit Feedback," Computer, vol. 40, no. 8, pp. 34-40, August, 2007.

	BibTex	x
	@article{ 10.1109/MC.2007.289, author = {Thorsten Joachims and Filip Radlinski}, title = {Search Engines that Learn from Implicit Feedback}, journal ={Computer}, volume = {40}, number = {8}, issn = {0018-9162}, year = {2007}, pages = {34-40}, doi = {http://doi.ieeecomputersociety.org/10.1109/MC.2007.289}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - MGZN JO - Computer TI - Search Engines that Learn from Implicit Feedback IS - 8 SN - 0018-9162 SP34 EP40 EPD - 34-40 A1 - Thorsten Joachims, A1 - Filip Radlinski, PY - 2007 KW - search KW - pairwise preferences KW - Osmot engine KW - machine learning VL - 40 JA - Computer ER -

Thorsten Joachims, Cornell University

Filip Radlinski, Cornell University

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.

Index Terms:

search, pairwise preferences, Osmot engine, machine learning

Citation:

Thorsten Joachims, Filip Radlinski, "Search Engines that Learn from Implicit Feedback," Computer, vol. 40, no. 8, pp. 34-40, Aug. 2007, doi:10.1109/MC.2007.289

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