Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines. (22nd October 2020)

• Record Type: Journal Article
• Authors: Altinok, Gulsah; Karagoz, Pinar; Batmaz, Inci
• Is Part Of: Computational intelligence. Volume 37:Number 1(2021)
• Page Start: 371
• Page End: 408
• Publication Date: 2020-10-22
• Subjects: artificial neural networks -- conic multivariate adaptive regression splines -- multivariate adaptive regression spline -- random forest -- support vector machines -- web search query; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12413
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: 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.