Direct Estimation of Position Bias for Unbiased Learning-to-Rank without Intervention
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The Unbiased Learning-to-Rank framework has been recently introduced as a general approach to systematically remove biases, such as position bias, from learning-to-rank models. The method takes two steps - estimating click propensities and using them to train unbiased models. Most common methods proposed in the literature for estimating propensities involve some degree of intervention in the live search engine. An alternative approach proposed recently uses an Expectation Maximization (EM) algorithm to estimate propensities by using ranking features for estimating relevances. In this work we propose a novel method to estimate propensities which does not use any intervention in live search or rely on any ranking features. Rather, we take advantage of the fact that the same query-document pair may naturally change ranks over time. This typically occurs for eCommerce search because of change of popularity of items over time, existence of time dependent ranking features, or addition or removal of items to the index (an item getting sold or a new item being listed). However, our method is general and can be applied to any search engine for which the rank of the same document may naturally change over time for the same query. We derive a simple likelihood function that depends on propensities only, and by maximizing the likelihood we are able to get estimates of the propensities. We apply this method to eBay search data to estimate click propensities for web and mobile search. We also use simulated data to show that the method gives reliable estimates of the "true" simulated propensities. Finally, we train a simple unbiased learning-to-rank model for eBay search using the estimated propensities and show that it outperforms the baseline model (which does not correct for position bias) on our offline evaluation metrics.
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