WassRank: Listwise Document Ranking Using Optimal Transport Theory
WassRank: Listwise Document Ranking Using Optimal Transport Theory
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DOI：
10.1145/3289600.3291006
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发表时间：
2019-01
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影响因子：
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通讯作者：
Haitao Yu;A. Jatowt;Hideo Joho;J. Jose;X. Yang;Long Chen
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作者：
Haitao Yu;A. Jatowt;Hideo Joho;J. Jose;X. Yang;Long Chen
排序学习在网络搜索、问答系统和推荐系统等领域有着重要的应用价值。本文的重点是列表式文档排名，其中与训练数据中相同查询相关联的所有文档都用作输入。我们提出了一种新的排名方法，称为WassRank，根据该方法，列表式文档排名的问题归结为学习最佳排名函数，实现最小Wasserstein距离的任务。具体来说，给定查询级预测和地面真值标签，我们首先将它们映射到两个概率向量。类似于最优运输问题，我们将每个概率向量视为一堆相关性质量，其峰值表示更高的相关性。列表排序损失被公式化为运输（或重塑）预测相关性质量堆的最小成本（Wasserstein距离），使得其匹配地面实况相关性质量堆。Wasserstein距离越小，预测就越接近地面事实。为了更好地捕获具有不同相关性标签的文档之间的固有的基于相关性的顺序信息，并降低具有相同相关性标签的文档的预测的方差，施加特定于排名的成本矩阵。为了验证WassRank的有效性，我们在两个基准集合上进行了一系列实验。实验结果表明：与四种非平凡的列表排序方法（即，LambdaRank、ListNet、ListMLE和ApxNDCG），WassRank可以在跨不同排名位置的nDCG和ERR方面实现显著改进的性能。具体而言，WassRank在nDCG@1方面相对于LambdaRank、ListNet、ListMLE和ApxNDCG的最大改进分别为15%、5%、7%、5%。
Learning to rank has been intensively studied and has shown great value in many fields, such as web search, question answering and recommender systems. This paper focuses on listwise document ranking, where all documents associated with the same query in the training data are used as the input. We propose a novel ranking method, referred to as WassRank, under which the problem of listwise document ranking boils down to the task of learning the optimal ranking function that achieves the minimum Wasserstein distance. Specifically, given the query level predictions and the ground truth labels, we first map them into two probability vectors. Analogous to the optimal transport problem, we view each probability vector as a pile of relevance mass with peaks indicating higher relevance. The listwise ranking loss is formulated as the minimum cost (the Wasserstein distance) of transporting (or reshaping) the pile of predicted relevance mass so that it matches the pile of ground-truth relevance mass. The smaller the Wasserstein distance is, the closer the prediction gets to the ground-truth. To better capture the inherent relevance-based order information among documents with different relevance labels and lower the variance of predictions for documents with the same relevance label, ranking-specific cost matrix is imposed. To validate the effectiveness of WassRank, we conduct a series of experiments on two benchmark collections. The experimental results demonstrate that: compared with four non-trivial listwise ranking methods (i.e., LambdaRank, ListNet, ListMLE and ApxNDCG), WassRank can achieve substantially improved performance in terms of nDCG and ERR across different rank positions. Specifically, the maximum improvements of WassRank over LambdaRank, ListNet, ListMLE and ApxNDCG in terms of nDCG@1 are 15%, 5%, 7%, 5%, respectively.