Title :
Search Engines that Learn from Implicit Feedback
Author :
Joachims, Thorsten ; Radlinski, Filip
Author_Institution :
Cornell Univ., Ithaca
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.
Keywords :
feedback; learning (artificial intelligence); query processing; search engines; implicit feedback; machine-learning techniques; search engines; search quality; training data; Bars; Chemistry; Data mining; Degradation; Feedback; Frequency; Packaging; Search engines; Osmot engine; machine learning; pairwise preferences; search;
DOI :
10.1109/MC.2007.289
