Search Engines that learn from implicit feedback / | |
Autor: | Joachims, Thorsten. |
Tema(s): | |
Resumen: | 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. Each time a user formulates a query or clicks on a search result, easily observable feedback is provided to the search engine. Unlike surveys or other types of explicit feedback, this implicit feedback is essentially free, reflects the search engine's natural use, and is specific to a particular user and collection |
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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. Each time a user formulates a query or clicks on a search result, easily observable feedback is provided to the search engine. Unlike surveys or other types of explicit feedback, this implicit feedback is essentially free, reflects the search engine's natural use, and is specific to a particular user and collection
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