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ES-Rank: evolution strategy learning to rank approach

Ibrahim, Osman Ali Sadek; Landa-Silva, Dario

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Authors

Osman Ali Sadek Ibrahim

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DARIO LANDA SILVA DARIO.LANDASILVA@NOTTINGHAM.AC.UK
Professor of Computational Optimisation



Abstract

Learning to Rank (LTR) is one of the current problems in Information Retrieval (IR) that attracts the attention from researchers. The LTR problem is mainly about ranking the retrieved documents for users in search engines, question answering and product recommendation systems. There are a number of LTR approaches from the areas of machine learning and computational intelligence. Most approaches have the limitation of being too slow or not being very effective. This paper investigates the application of evolutionary computation, specifically a (1+1) Evolutionary Strategy called ES-Rank, to tackle the LTR problem. Experimental results from comparing the proposed method to fourteen other approaches from the literature, show that ESRank achieves the overall best performance. Three datasets (MQ2007, MQ2008 and MSLR-WEB10K) from the LETOR benchmark collection and two performance metrics, Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG) at top-10 query-document pairs retrieved, were used in the experiments. The contribution of this paper is an effective and efficient method for the LTR problem.

Citation

Ibrahim, O. A. S., & Landa-Silva, D. (2017). ES-Rank: evolution strategy learning to rank approach. In SAC '17: Proceedings of the Symposium on Applied Computing (944-950). https://doi.org/10.1145/3019612.3019696

Conference Name 32nd ACM Symposium on Applied Computing (SAC 2017)
Conference Location Marrakech, Morocco
Start Date Apr 3, 2017
End Date Apr 7, 2017
Acceptance Date Nov 28, 2016
Publication Date Apr 3, 2017
Deposit Date Jun 13, 2017
Publicly Available Date Mar 28, 2024
Peer Reviewed Peer Reviewed
Pages 944-950
Book Title SAC '17: Proceedings of the Symposium on Applied Computing
ISBN 9781450344869
DOI https://doi.org/10.1145/3019612.3019696
Keywords Learning to Rank; Evolution Strategy; Machine Learning;
Information Retrieval
Public URL https://nottingham-repository.worktribe.com/output/854560
Publisher URL http://dl.acm.org/citation.cfm?doid=3019612.3019696
Additional Information Published in: Proceedings of the Symposium on Applied Computing (SAC'17), 3-7 April 2017, Marrakech, Morocco, pp. 944-950. New York : ACM, 2017. ISBN 978-1-4503-4486-9. doi:10.1145/3019612.3019696

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