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NeuralNDCG: Direct Optimisation of a Ranking Metric via Differentiable Relaxation of Sorting
As a result, we obtain a new ranking loss function which is an arbitrarily accurate approximation to the evaluation metric, thus closing the gap between the training and the evaluation of LTR models.
Context-Aware Learning to Rank with Self-Attention
In learning to rank, one is interested in optimising the global ordering of a list of items according to their utility for users. Popular approaches learn a scoring function that scores items individually (i. e. without the context of other items in the list) by optimising a pointwise, pairwise or listwise loss.
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