Besides position bias, which has been well-studied, trust bias is another
type of bias prevalent in user interactions with rankings: users are more
likely to click incorrectly w.r.t. their preferences on highly ranked items
because they trust the ranking system. While previous work has observed this
behavior in users, we prove that existing Counterfactual Learning to Rank
(CLTR) methods do not remove this bias, including methods specifically designed
to mitigate this type of bias. Moreover, we prove that Inverse Propensity
Scoring (IPS) is principally unable to correct for trust bias under non-trivial
circumstances. Our main contribution is a new estimator based on affine
corrections: it both reweights clicks and penalizes items displayed on ranks
with high trust bias. Our estimator is the first estimator that is proven to
remove the effect of both trust bias and position bias. Furthermore, we show
that our estimator is a generalization of the existing CLTR framework: if no
trust bias is present, it reduces to the original IPS estimator. Our
semi-synthetic experiments indicate that by removing the effect of trust bias
in addition to position bias, CLTR can approximate the optimal ranking system
even closer than previously possible.

研究了点击偏见与信任偏见的存在，提出了一种基于仿射修正的估算器，该估算器是目前唯一被证明能够去除二者影响的，并利用半合成实验证明在消除这些偏见的情况下，CLTR 可以更接近最佳排名系统。