Existing work in counterfactual Learning to Rank (LTR) has focussed on
optimizing feature-based models that predict the optimal ranking based on
document features. LTR methods based on bandit algorithms often optimize
tabular models that memorize the optimal ranking per query. These types of
model have their own advantages and disadvantages. Feature-based models provide
very robust performance across many queries, including those previously unseen,
however, the available features often limit the rankings the model can predict.
In contrast, tabular models can converge on any possible ranking through
memorization. However, memorization is extremely prone to noise, which makes
tabular models reliable only when large numbers of user interactions are
available. Can we develop a robust counterfactual LTR method that pursues
memorization-based optimization whenever it is safe to do? We introduce the
Generalization and Specialization (GENSPEC) algorithm, a robust feature-based
counterfactual LTR method that pursues per-query memorization when it is safe
to do so. GENSPEC optimizes a single feature-based model for generalization:
robust performance across all queries, and many tabular models for
specialization: each optimized for high performance on a single query. GENSPEC
uses novel relative high-confidence bounds to choose which model to deploy per
query. By doing so, GENSPEC enjoys the high performance of successfully
specialized tabular models with the robustness of a generalized feature-based
model. Our results show that GENSPEC leads to optimal performance on queries
with sufficient click data, while having robust behavior on queries with little
or noisy data.

提出了一种名为 GENSPEC 的算法，它是一个功能强大的反事实 LTR 方法，可以在安全的情况下追求基于记忆的优化，同时保持强大稳健的表现。 GENSPEC 通过使用新颖的相对高置信度界限来选择每个查询要部署哪个模型，从而实现了成功专业化的分表模型的高性能，同时具有广义特征模型的稳健性。

计数事实学习中的稳健泛化和安全查询特化

Robust Generalization and Safe Query-Specialization in Counterfactual  Learning to Rank

This tutorial covers and contrasts the two main methodologies in unbiased
Learning to Rank (LTR): Counterfactual LTR and Online LTR. There has long been
an interest in LTR from user interactions, however, this form of implicit
feedback is very biased. In recent years, unbiased LTR methods have been
introduced to remove the effect of different types of bias caused by
user-behavior in search. For instance, a well addressed type of bias is
position bias: the rank at which a document is displayed heavily affects the
interactions it receives. Counterfactual LTR methods deal with such types of
bias by learning from historical interactions while correcting for the effect
of the explicitly modelled biases. Online LTR does not use an explicit user
model, in contrast, it learns through an interactive process where randomized
results are displayed to the user. Through randomization the effect of
different types of bias can be removed from the learning process. Though both
methodologies lead to unbiased LTR, their approaches differ considerably,
furthermore, so do their theoretical guarantees, empirical results, effects on
the user experience during learning, and applicability. Consequently, for
practitioners the choice between the two is very substantial. By providing an
overview of both approaches and contrasting them, we aim to provide an
essential guide to unbiased LTR so as to aid in understanding and choosing
between methodologies.

这篇论文讨论了无偏学习排名（LTR）中的两种方法论：对联合 LTR 和在线 LTR，介绍了它们的差异和对用户体验和学习的影响，并提供了选择适当方法的实践指南。