Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines

Learning to rankis 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 onpoint-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.

Automated summary

Learning to rank is a supervised learning problem that aims to construct a ranking model for a given dataset, with MARS and CMARS being effective techniques for point-wise learning to rank. Experimental results show that MARS and ANN are effective methods for learning to rank problem and provide promising results.