Active exploration for learning rankings from clickthrough data

This work develops a Bayesian approach for selecting rankings to present users so that interactions result in more informative training data and finds that active exploration substantially outperformassive observation and random exploration.

Abstract

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.