Abstract
This paper addresses listwise approaches in learning to rank for Information Retrieval(IR). The listwise losses are built on the probability of ranking a document highest among the documents set. The probability treats all the documents equally. However, the documents with higher ranks should be emphasized in IR where the ranking order on the top of the ranked list is crucial. In this paper, we establish a framework for cost-sensitive listwise approaches. The framework redefines the probability by imposing weights for the documents. The framework reduces the task of weighting the documents to the task of weighting the document pairs. The weights of the document pairs are computed based on Normalized Discounted Cumulative Gain(NDCG). It is proven that the losses of cost-sensitive listwise approaches are the upper bound of the NDCG loss. As an example, we propose a cost-sensitive ListMLE method. Empirical results shows the advantage of the proposed method.
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Lu, M., Xie, M., Wang, Y., Liu, J., Huang, Y. (2010). Cost-Sensitive Listwise Ranking Approach. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13657-3_39
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DOI: https://doi.org/10.1007/978-3-642-13657-3_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13656-6
Online ISBN: 978-3-642-13657-3
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