A Comparative Study on Learning to Rank with Computational Methods

Date

2017-12-14

Author

Batmaz, İnci
Karagöz, Pınar

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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.

Subject Keywords

Web search query, Artificial neural networks (ANN), Support vector machines (SVM), Multivariate adaptive regression spline (MARS), Conic multivariate adaptive regression splines (CMARS)

URI

https://hdl.handle.net/11511/54809

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Department of Statistics, Conference / Seminar

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Citation Formats

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