A Domain Generalization Perspective on Listwise Context Modeling
This addresses the issue of ranking generalization for unseen queries in information retrieval, which is incremental as it builds on existing LETOR methods.
The paper tackles the problem of inter-query variability in learning-to-rank by proposing a domain generalization strategy, resulting in QILCM outperforming previous state-of-the-art approaches by a substantial margin on benchmark datasets.
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.