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.

Source link