A Domain Generalization Perspective on Listwise Context Modeling


  • Lin Zhu Ctrip
  • Yihong Chen Ctrip
  • Bowen He Ctrip




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 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.




How to Cite

Zhu, L., Chen, Y., & He, B. (2019). A Domain Generalization Perspective on Listwise Context Modeling. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 5965-5972. https://doi.org/10.1609/aaai.v33i01.33015965



AAAI Technical Track: Machine Learning