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.

通过介入感知估计器建议一种优化基于用户交互的排名系统的新方法，以消除位置偏差、信任偏差和项目选择偏差。实验结果表明，相比现有方法，该新方法可以更有效地受益于在线干预。