A Literature Review on Methods for Learning to Rank

Junior Zilles; Giancarlo Lucca; Eduardo Nunes Borges

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Paper

A Literature Review on Methods for Learning to Rank

Topics: Industrial Applications of Artificial Intelligence; Neural Network Software and Applications; Performance Evaluation and Benchmarking; Problem Solving

In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS, 545-552, 2022

Authors: Junior Zilles ; Giancarlo Lucca and Eduardo Nunes Borges

Affiliation: Centro de Ciências Computacionais, Universidade Federal do Rio Grande – FURG, Av. Itália, km 8, Rio Grande, RS, 96203-900, Brazil

Keyword(s): Information Retrieval, Learning to Rank, Algorithms.

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.

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Paper citation in several formats:

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 1: ICEIS; ISBN 978-989-758-569-2; ISSN 2184-4992, SciTePress, pages 545-552. DOI: 10.5220/0011065600003179

@conference{iceis22,
author={Junior Zilles. and Giancarlo Lucca. and Eduardo Nunes Borges.},
title={A Literature Review on Methods for Learning to Rank},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2022},
pages={545-552},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011065600003179},
isbn={978-989-758-569-2},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Literature Review on Methods for Learning to Rank
SN - 978-989-758-569-2
IS - 2184-4992
AU - Zilles, J.
AU - Lucca, G.
AU - Borges, E.
PY - 2022
SP - 545
EP - 552
DO - 10.5220/0011065600003179
PB - SciTePress