Feature attributions are a commonly used explanation type, when we want to posthoc explain the prediction of a trained model. Yet, they are not very well explored in IR. Importantly, feature attribution has rarely been rigorously defined, beyond attributing the most important feature the highest value. What it means for a feature to be more important than others is often left vague. Consequently, most approaches focus on just selecting the most important features and under utilize or even ignore the relative importance within features. In this work, we rigorously define the notion of feature attribution for ranking models, and list essential properties that a valid attribution should have. We then propose RankingSHAP as a concrete instantiation of a list-wise ranking attribution method. Contrary to current explanation evaluation schemes that focus on selections, we propose two novel evaluation paradigms for evaluating attributions over learning-to-rank models. We evaluate RankingSHAP for commonly used learning-to-rank datasets to showcase the more nuanced use of an attribution method while highlighting the limitations of selection-based explanations. In a simulated experiment we design an interpretable model to demonstrate how list-wise ranking attributes can be used to investigate model decisions and evaluate the explanations qualitatively. Because of the contrastive nature of the ranking task, our understanding of ranking model decisions can substantially benefit from feature attribution explanations like RankingSHAP.

中文翻译：

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RankingSHAP——排序模型的列表特征归因解释

当我们想要事后解释训练模型的预测时，特征归因是一种常用的解释类型。然而，它们在红外领域还没有得到很好的探索。重要的是，除了将最重要的特征赋予最高价值之外，特征归因很少被严格定义。一项功能比其他功能更重要意味着什么，这通常是模糊的。因此，大多数方法只关注选择最重要的特征，而没有充分利用甚至忽略特征内的相对重要性。在这项工作中，我们严格定义了排名模型的特征归因概念，并列出了有效归因应具有的基本属性。然后，我们提出 RankingSHAP 作为列表式排名归因方法的具体实例。与当前侧重于选择的解释评估方案相反，我们提出了两种新颖的评估范式，用于评估学习排名模型的归因。我们评估常用的学习排名数据集的 RankingSHAP，以展示归因方法的更细致的使用，同时强调基于选择的解释的局限性。在模拟实验中，我们设计了一个可解释的模型，以演示如何使用列表排序属性来研究模型决策并定性评估解释。由于排名任务的对比性质，我们对排名模型决策的理解可以从像 RankingSHAP 这样的特征归因解释中受益匪浅。