  Interpretability of learning-to-rank models is a crucial yet relatively
under-examined research area. Recent progress on interpretable ranking models
largely focuses on generating post-hoc explanations for existing black-box
ranking models, whereas the alternative option of building an intrinsically
interpretable ranking model with transparent and self-explainable structure
remains unexplored. Developing fully-understandable ranking models is necessary
in some scenarios (e.g., due to legal or policy constraints) where post-hoc
methods cannot provide sufficiently accurate explanations. In this paper, we
lay the groundwork for intrinsically interpretable learning-to-rank by
introducing generalized additive models (GAMs) into ranking tasks. Generalized
additive models (GAMs) are intrinsically interpretable machine learning models
and have been extensively studied on regression and classification tasks. We
study how to extend GAMs into ranking models which can handle both item-level
and list-level features and propose a novel formulation of ranking GAMs. To
instantiate ranking GAMs, we employ neural networks instead of traditional
splines or regression trees. We also show that our neural ranking GAMs can be
distilled into a set of simple and compact piece-wise linear functions that are
much more efficient to evaluate with little accuracy loss. We conduct
experiments on three data sets and show that our proposed neural ranking GAMs
can achieve significantly better performance than other traditional GAM
baselines while maintaining similar interpretability.
