  As one of the most popular techniques for solving the ranking problem in
information retrieval, Learning-to-rank (LETOR) has received a lot of attention
both in academia and industry due to its importance in a wide variety of data
mining applications. However, most of existing LETOR approaches choose to learn
a single global ranking function to handle all queries, and ignore the
substantial differences that exist between queries. In this paper, we propose a
domain generalization strategy to tackle this problem. We propose
Query-Invariant Listwise Context Modeling (QILCM), a novel neural architecture
which eliminates the detrimental influence of inter-query variability by
learning \textit{query-invariant} latent representations, such that the ranking
system could generalize better to unseen queries. We evaluate our techniques on
benchmark datasets, demonstrating that QILCM outperforms previous
state-of-the-art approaches by a substantial margin.
