Unbiased CLTR requires click propensities to compensate for the difference
between user clicks and true relevance of search results via IPS. Current
propensity estimation methods assume that user click behavior follows the PBM
and estimate click propensities based on this assumption. However, in reality,
user clicks often follow the CM, where users scan search results from top to
bottom and where each next click depends on the previous one. In this cascade
scenario, PBM-based estimates of propensities are not accurate, which, in turn,
hurts CLTR performance. In this paper, we propose a propensity estimation
method for the cascade scenario, called CM-IPS. We show that CM-IPS keeps CLTR
performance close to the full-information performance in case the user clicks
follow the CM, while PBM-based CLTR has a significant gap towards the
full-information. The opposite is true if the user clicks follow PBM instead of
the CM. Finally, we suggest a way to select between CM- and PBM-based
propensity estimation methods based on historical user clicks.

本文提出了一种针对级联点击模式（CM）场景下的倾向性估计方法（CM-IPS），该方法在用户遵循 CM 点击搜索结果并具有极高表现的情况下，能使 CLTR 表现保持接近于全信息量表现，同时在用户遵循 PBM（Poisson Binomial Model）情况下，该方法不仅表现不佳，反而导致更大偏差，因此需要根据历史用户点击数据来选择使用 CM-IPS 或 PBM-based 倾向性估计方法。