A Comparative Study on Learning to Rank with Computational Methods

2017-12-14
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.
Citation Formats
İ. Batmaz, P. Karagöz, and G. Serdar, “A Comparative Study on Learning to Rank with Computational Methods,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54809.