# Valid Explanations for Learning to Rank Models

arXiv: https://arxiv.org/abs/2004.13972 · HTML: https://mlsanity.com/paper/2004.13972/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks](https://mlsanity.com/paper/2105.08621/) (2021)
- [Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank](https://mlsanity.com/paper/2212.12722/) (2022)
- [RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank](https://mlsanity.com/paper/2405.01848/) (2024)
- [ExaRanker: Explanation-Augmented Neural Ranker](https://mlsanity.com/paper/2301.10521/) (2023)

## Abstract (arXiv)

Learning-to-rank (LTR) is a class of supervised learning techniques that apply to ranking problems dealing with a large number of features. The popularity and widespread application of LTR models in prioritizing information in a variety of domains makes their scrutability vital in today's landscape of fair and transparent learning systems. However, limited work exists that deals with interpreting the decisions of learning systems that output rankings. In this paper we propose a model agnostic local explanation method that seeks to identify a small subset of input features as explanation to a ranking decision. We introduce new notions of validity and completeness of explanations specifically for rankings, based on the presence or absence of selected features, as a way of measuring goodness. We devise a novel optimization problem to maximize validity directly and propose greedy algorithms as solutions. In extensive quantitative experiments we show that our approach outperforms other model agnostic explanation approaches across pointwise, pairwise and listwise LTR models in validity while not compromising on completeness.