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


COMPUTATIONAL INTELLIGENCE, vol.37, no.1, pp.371-408, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.1111/coin.12413
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, INSPEC, Psycinfo, zbMATH
  • Page Numbers: pp.371-408
  • Keywords: artificial neural networks, conic multivariate adaptive regression splines, multivariate adaptive regression spline, random forest, support vector machines, web search query, CMARS
  • Middle East Technical University Affiliated: Yes


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.