A Literature Review on Methods for Learning to Rank

Junior Zilles, Giancarlo Lucca, Eduardo Borges

2022

Abstract

The increasing number of indexed documents makes manual retrieval almost impossible when they are retrieved or stored automatically. The solution to this problem consists of using information retrieval systems, which seek to present the most relevant data items to the user in order of relevance. Therefore, this work aims to conduct a theoretical survey of the most used algorithms in the Information Retrieval field using Learning to Rank methods. We also provide an analysis regarding the datasets used as benchmarks in the literature. We observed that RankSVM and LETOR collection are the most frequent method and datasets employed in the analyzed works.

Paper Citation

in Harvard Style

Zilles J., Lucca G. and Borges E. (2022). A Literature Review on Methods for Learning to Rank. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-569-2, pages 545-552. DOI: 10.5220/0011065600003179

in Bibtex Style

@conference{iceis22,
author={Junior Zilles and Giancarlo Lucca and Eduardo Borges},
title={A Literature Review on Methods for Learning to Rank},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2022},
pages={545-552},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011065600003179},
isbn={978-989-758-569-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - A Literature Review on Methods for Learning to Rank
SN - 978-989-758-569-2
AU - Zilles J.
AU - Lucca G.
AU - Borges E.
PY - 2022
SP - 545
EP - 552
DO - 10.5220/0011065600003179