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Active Exploration For Learning To RankSearch engine logs are a well recognized source of training data for learning to rank. However, previous work has only considered logs collected passively. We show that an active exploration strategy that actively selects rankings to show can provide training data that leads to faster learning. Specifically, we develop a Bayesian approach for selecting rankings to present users so that interactions result in more informative training data. Our results using the TREC-10 Web corpus, as well as on synthetic data, demonstrate that a directed exploration strategy quickly leads to users being presented improved rankings in an online learning setting. We find that active exploration substantially outperforms passive observation and random exploration. |