  We propose Conditional Imputation GAN, an extended missing data imputation
method based on Generative Adversarial Networks (GANs). The motivating use case
is learning-to-rank, the cornerstone of modern search, recommendation system,
and information retrieval applications. Empirical ranking datasets do not
always follow standard Gaussian distributions or Missing Completely At Random
(MCAR) mechanism, which are standard assumptions of classic missing data
imputation methods. Our methodology provides a simple solution that offers
compatible imputation guarantees while relaxing assumptions for missing
mechanisms and sidesteps approximating intractable distributions to improve
imputation quality. We prove that the optimal GAN imputation is achieved for
Extended Missing At Random (EMAR) and Extended Always Missing At Random (EAMAR)
mechanisms, beyond the naive MCAR. Our method demonstrates the highest
imputation quality on the open-source Microsoft Research Ranking (MSR) Dataset
and a synthetic ranking dataset compared to state-of-the-art benchmarks and
across various feature distributions. Using a proprietary Amazon Search ranking
dataset, we also demonstrate comparable ranking quality metrics for ranking
models trained on GAN-imputed data compared to ground-truth data.
