论文标题
排名：学习排名
Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank
论文作者
论文摘要
理解为什么模型在适应现实世界决策时至关重要。石灰是用于分类和回归任务的流行模型 - 不合SNOSTIC特征归因方法。但是，与分类或回归相比，学习在信息检索中排名的任务更为复杂。在这项工作中，我们将Lime扩展到提出Rank-Lime，这是一种模型不合时宜的，局部，事后的线性特征归因方法，用于学习排名的任务，以生成排名列表的解释。 我们采用新颖的基于相关的扰动，可区分的排名损失函数，并引入新的指标来评估基于排名的附加特征归因模型。我们将排行榜与各种竞争系统进行比较，并在MS MARCO数据集中训练了模型，并观察到，在模型保真度和解释NDCG方面，排名lime优于现有的解释算法。因此，我们提出了第一个生成用于解释排名列表的加法功能属性的算法之一。
Understanding why a model makes certain predictions is crucial when adapting it for real world decision making. LIME is a popular model-agnostic feature attribution method for the tasks of classification and regression. However, the task of learning to rank in information retrieval is more complex in comparison with either classification or regression. In this work, we extend LIME to propose Rank-LIME, a model-agnostic, local, post-hoc linear feature attribution method for the task of learning to rank that generates explanations for ranked lists. We employ novel correlation-based perturbations, differentiable ranking loss functions and introduce new metrics to evaluate ranking based additive feature attribution models. We compare Rank-LIME with a variety of competing systems, with models trained on the MS MARCO datasets and observe that Rank-LIME outperforms existing explanation algorithms in terms of Model Fidelity and Explain-NDCG. With this we propose one of the first algorithms to generate additive feature attributions for explaining ranked lists.
