  Optimizing ranking systems based on user interactions is a well-studied
problem. State-of-the-art methods for optimizing ranking systems based on user
interactions are divided into online approaches - that learn by directly
interacting with users - and counterfactual approaches - that learn from
historical interactions. Existing online methods are hindered without online
interventions and thus should not be applied counterfactually. Conversely,
counterfactual methods cannot directly benefit from online interventions. We
propose a novel intervention-aware estimator for both counterfactual and online
Learning to Rank (LTR). With the introduction of the intervention-aware
estimator, we aim to bridge the online/counterfactual LTR division as it is
shown to be highly effective in both online and counterfactual scenarios. The
estimator corrects for the effect of position bias, trust bias, and
item-selection bias by using corrections based on the behavior of the logging
policy and on online interventions: changes to the logging policy made during
the gathering of click data. Our experimental results, conducted in a
semi-synthetic experimental setup, show that, unlike existing counterfactual
LTR methods, the intervention-aware estimator can greatly benefit from online
interventions.
