A Comparative Study on Learning to Rank with Computational Methods


BATMAZ İ. , Karagoz P. , Serdar G.

IEEE International Conference on Big Data (IEEE Big Data), Massachusetts, United States Of America, 11 - 14 December 2017, pp.1898-1906 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Massachusetts
  • Country: United States Of America
  • Page Numbers: pp.1898-1906

Abstract

Learning to rank is a supervised learning problem that aims to construct a ranking model. The most common application of learning to rank is to rank a set of documents against a query. In this work, we focus on pointwise approach and compare the performances of four computational methods in developing ranking models using several criteria such as accuracy, stability and robustness. The experimental results show that Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Networks (ANN) are effective methods for learning to rank problem and provide promising results.