@article{Zhu_Chen_He_2019, title={A Domain Generalization Perspective on Listwise Context Modeling}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/4548}, DOI={10.1609/aaai.v33i01.33015965}, abstractNote={<p>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 QueryInvariant Listwise Context Modeling (QILCM), a novel neural architecture which eliminates the detrimental influence of inter-query variability by learning <em>query-invariant</em> 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.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhu, Lin and Chen, Yihong and He, Bowen}, year={2019}, month={Jul.}, pages={5965-5972} }