Abstract
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Listwise learning to rank based on approximate rank indicators
at
| Thibaut Thonet, Yagmur Gizem Cinar, Eric Gaussier, Minghan Li, Jean-Michel Renders |
| The Thirty-Sixth AAAI Conference on Artificial Intelligence, virtual event, 22 February - 1 March, 2022 |
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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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