Listwise Learning to Rank Based on Approximate Rank Indicators

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.

Authors

• Thibaut Thonet
• Yagmur Gizem Cinar
• Eric Gaussier
• Minghan Li
• Jean-Michel Renders

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