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Learning-to-rank (LTR) is a class of supervised learning techniques thatapply to ranking problems dealing with a large number of features.  The popularity and widespread application of LTR models in prioritizinginformation in a variety of domains makes their scrutability vital in today'slandscape of fair and transparent learning systems. However, limited workexists that deals with interpreting the decisions of learning systems thatoutput rankings. In this paper we propose a model agnostic local explanationmethod that seeks to identify a small subset of input features as explanationto a ranking decision. We introduce new notions of validity and completeness ofexplanations specifically for rankings, based on the presence or absence ofselected features, as a way of measuring goodness. We devise a noveloptimization problem to maximize validity directly and propose greedyalgorithms as solutions. In extensive quantitative experiments we show that ourapproach outperforms other model agnostic explanation approaches acrosspointwise, pairwise and listwise LTR models in validity while not compromisingon completeness.

Valid Explanations for Learning to Rank Models