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