The propensity model introduced by Jain et al. 2016 has become a standard
approach for dealing with missing and long-tail labels in extreme multi-label
classification (XMLC). In this paper, we critically revise this approach
showing that despite its theoretical soundness, its application in contemporary
XMLC works is debatable. We exhaustively discuss the flaws of the
propensity-based approach, and present several recipes, some of them related to
solutions used in search engines and recommender systems, that we believe
constitute promising alternatives to be followed in XMLC.

本文对 Jain 等人在 2016 年提出的倾向模型在多标签分类中的应用进行了批判性修订，并提出了几种替代方案，以解决现代多标签分类工作中倾向基础方法的缺陷。

极端多标签分类中的缺失标签、长尾和倾向性问题

On Missing Labels, Long-tails and Propensities in Extreme Multi-label  Classification

Extreme multi-label classification (XMLC) refers to the task of tagging
instances with small subsets of relevant labels coming from an extremely large
set of all possible labels. Recently, XMLC has been widely applied to diverse
web applications such as automatic content labeling, online advertising, or
recommendation systems. In such environments, label distribution is often
highly imbalanced, consisting mostly of very rare tail labels, and relevant
labels can be missing. As a remedy to these problems, the propensity model has
been introduced and applied within several XMLC algorithms. In this work, we
focus on the problem of optimal predictions under this model for probabilistic
label trees, a popular approach for XMLC problems. We introduce an inference
procedure, based on the $A^*$-search algorithm, that efficiently finds the
optimal solution, assuming that all probabilities and propensities are known.
We demonstrate the attractiveness of this approach in a wide empirical study on
popular XMLC benchmark datasets.

本研究提出一种基于 A * 搜索算法的推断过程，能够有效地解决了在极端多标签分类模型中标签分布不均、稀疏、缺失等问题，从而优化了分类预测效果。

倾向分数概率标签树

Propensity-scored Probabilistic Label Trees

Learning to rank with biased click data is a well-known challenge. A variety
of methods has been explored to debias click data for learning to rank such as
click models, result interleaving and, more recently, the unbiased
learning-to-rank framework based on inverse propensity weighting. Despite their
differences, most existing studies separate the estimation of click bias
(namely the \textit{propensity model}) from the learning of ranking algorithms.
To estimate click propensities, they either conduct online result
randomization, which can negatively affect the user experience, or offline
parameter estimation, which has special requirements for click data and is
optimized for objectives (e.g. click likelihood) that are not directly related
to the ranking performance of the system. In this work, we address those
problems by unifying the learning of propensity models and ranking models. We
find that the problem of estimating a propensity model from click data is a
dual problem of unbiased learning to rank. Based on this observation, we
propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker
and an \textit{unbiased propensity model}. DLA is an automatic unbiased
learning-to-rank framework as it directly learns unbiased ranking models from
biased click data without any preprocessing. It can adapt to the change of bias
distributions and is applicable to online learning. Our empirical experiments
with synthetic and real-world data show that the models trained with DLA
significantly outperformed the unbiased learning-to-rank algorithms based on
result randomization and the models trained with relevance signals extracted by
click models.

本文提出了一种名为 DLA 的双重学习算法，该算法可以联合学习无偏好排名和无偏好倾向模型，以直接从有偏好的点击数据中学习无偏好排名模型，避免了对点击数据的特殊要求，实现了自适应学习和在线学习。实验结果表明，使用 DLA 训练的模型明显优于基于结果随机化和基于点击模型提取的相关信号的无偏好学习至排名算法。