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

A feature-based model explanation denotes how much each input feature
contributes to a model's output for a given data point. As the number of
proposed explanation functions grows, we lack quantitative evaluation criteria
to help practitioners know when to use which explanation function. This paper
proposes quantitative evaluation criteria for feature-based explanations: low
sensitivity, high faithfulness, and low complexity. We devise a framework for
aggregating explanation functions. We develop a procedure for learning an
aggregate explanation function with lower complexity and then derive a new
aggregate Shapley value explanation function that minimizes sensitivity.

该论文提出了基于特征的模型解释的定量评估标准和聚合解释函数的框架，并开发了一个降低复杂度的新的 Shapley 值解释函数。