Listwise Learning to Rank Based on Approximate Rank Indicators


  • Thibaut Thonet NAVER LABS Europe
  • Yagmur Gizem Cinar Amazon
  • Eric Gaussier Univ. Grenoble Alpes, CNRS, LIG
  • Minghan Li Univ. Grenoble Alpes, CNRS, LIG
  • Jean-Michel Renders NAVER LABS Europe



Machine Learning (ML)


We study here a way to approximate information retrieval metrics through a softmax-based approximation of the rank indicator function. Indeed, this latter function is a key component in the design of information retrieval metrics, as well as in the design of the ranking and sorting functions. Obtaining a good approximation for it thus opens the door to differentiable approximations of many evaluation measures that can in turn be used in neural end-to-end approaches. We first prove theoretically that the approximations proposed are of good quality, prior to validate them experimentally on both learning to rank and text-based information retrieval tasks.




How to Cite

Thonet, T., Cinar, Y. G., Gaussier, E., Li, M., & Renders, J.-M. (2022). Listwise Learning to Rank Based on Approximate Rank Indicators. Proceedings of the AAAI Conference on Artificial Intelligence, 36(8), 8494-8502.



AAAI Technical Track on Machine Learning III