de Rijke, M.

Jagerman, R.

Kiseleva, J.

English

List-wise learning to rank methods are considered to be the state-of-the-art. One of the major problems with these methods is that the ambiguous nature of relevance labels in learning to rank data is ignored. Ambiguity of relevance labels refers to the phenomenon that multiple documents may be assigned the same relevance label for a given query, so that no preference order should be learned for those documents. In this paper we propose a novel sampling technique for computing a list-wise loss that can take into account this ambiguity. We show the effectiveness of the proposed method by training a 3-layer deep neural network. We compare our new loss function to two strong baselines: ListNet and ListMLE. We show that our method generalizes better and significantly outperforms other methods on the validation and test sets

International Migration, Integration and Social Cohesion online publications

Modeling Label Ambiguity for List-Wise Neural Learning to Rank

https://pure.uva.nl/ws/files/37135020/1707.07493.pdf

Modeling Label Ambiguity for List-Wise Neural Learning to Rank

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International Migration, Integration and Social Cohesion online publications