Listwise Learning to Rank Based on Approximate Rank Indicators

Authors

  • 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

DOI:

https://doi.org/10.1609/aaai.v36i8.20826

Keywords:

Machine Learning (ML)

Abstract

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.

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Published

2022-06-28

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. https://doi.org/10.1609/aaai.v36i8.20826

Issue

Section

AAAI Technical Track on Machine Learning III