document: Active exploration for learning rankings from clickthrough data We address the task of learning rankings of documents from search enginelogs of user behavior . Previous work on this problem has relied onpassively collected clickthrough data . In contrast , we show that anactive exploration strategy can provide data that leads to much fasterlearning . Specifically , we develop a Bayesian approach for selectingrankings to present users so that interactions result in more informativetraining data . Our results using the TREC-10 Web corpus , as well assynthetic data , demonstrate that a directed exploration strategy quicklyleads to users being presented improved rankings in an online learningsetting . We find that active exploration substantially outperformspassive observation and random exploration .
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