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Search Engines that Learn from Implicit Feedback
August 2007 (vol. 40 no. 8)
pp. 34-40
ASCII Text
x 
 
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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