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An Alternative Cross Entropy Loss for Learning-to-Rank
[article]
2020
arXiv
pre-print
Listwise learning-to-rank methods form a powerful class of ranking algorithms that are widely adopted in applications such as information retrieval. These algorithms learn to rank a set of items by optimizing a loss that is a function of the entire set---as a surrogate to a typically non-differentiable ranking metric. Despite their empirical success, existing listwise methods are based on heuristics and remain theoretically ill-understood. In particular, none of the empirically-successful loss
arXiv:1911.09798v4
fatcat:tmkyuzaq6bgmdjbiikatlko34m