Abstract
The effectiveness of the cost-sensitive listwise approach has been verified in learning to rank. However, the order preservation and generalization of cost-sensitive listwise approach are not studied. The two properties are very important since they can guide to develop a better ranking method. In this paper, we establish a framework for order preserved cost-sensitive listwise ranking approach. The framework yields the conditions of order preservation for the cost-sensitive listwise method. In addition, the generalization of the order preserved cost-sensitive listwise approach is proven. According to the theorem of generalization, a novel loss function for order preserved cost-sensitive listwise approach has been proposed. The loss function not only is order preserved but also penalizes the model complexity by an auxiliary variable. As an example, we propose the order preserved cost-sensitive ListMLE algorithm. Experimental results show the proposed method outperforms the baselines.
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Lu, M., Xie, M., Wang, Y., Liu, J., Huang, Y. (2010). Order Preserved Cost-Sensitive Listwise Approach in Learning to Rank. In: Cheng, PJ., Kan, MY., Lam, W., Nakov, P. (eds) Information Retrieval Technology. AIRS 2010. Lecture Notes in Computer Science, vol 6458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17187-1_19
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DOI: https://doi.org/10.1007/978-3-642-17187-1_19
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-642-17187-1
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