Learning to rank is a supervised learning problem that aims to construct a ranking model for the given data. The most common application of learning to rank is to rank a set of documents against a query. In this work, we focus on point‐wise learning to rank, where the model learns the ranking values. Multivariate adaptive regression splines (MARS) and conic multivariate adaptive regression splines (CMARS) are supervised learning techniques that have been proven to provide successful results on various prediction problems. In this article, we investigate the effectiveness of MARS and CMARS for point‐wise learning to rank problem. The prediction performance is analyzed in comparison to three well‐known supervised learning methods, artificial neural network (ANN), support vector machine, and random forest for two datasets under a variety of metrics including accuracy, stability, and robustness. The experimental results show that MARS and ANN are effective methods for learning to rank problem and provide promising results.

中文翻译：

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通过使用多元自适应回归样条和圆锥多元自适应回归样条学习排名

学习排名是有监督的学习问题，旨在为给定数据构建排名模型。学习排名最常见的应用是针对查询对一组文档进行排名。在这项工作中，我们专注于点学习以进行排名，模型在其中学习排名值。多元自适应回归样条（MARS）和圆锥形多元自适应回归样条（CMARS）是有监督的学习技术，已被证明可以为各种预测问题提供成功的结果。在本文中，我们研究了MARS和CMARS在点学习中对问题进行排名的有效性。与三种著名的有监督学习方法，人工神经网络（ANN），支持向量机和两个数据集的随机森林进行了对比，分析了两种性能的预测性能，这些指标包括准确性，稳定性和鲁棒性。实验结果表明，MARS和ANN是学习问题排名和提供有希望的结果的有效方法。