ES-Rank: evolution strategy learning to rank approach

Ibrahim, Osman Ali Sadek and Landa-Silva, Dario (2017) ES-Rank: evolution strategy learning to rank approach. In: 32nd ACM Symposium on Applied Computing (SAC 2017), 3-7 April 2017, Marrakech, Morocco.

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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.

Item Type: Conference or Workshop Item (Paper)
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
Keywords: Learning to Rank; Evolution Strategy; Machine Learning; Information Retrieval
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Landa-Silva, Dario
Date Deposited: 13 Jun 2017 09:37
Last Modified: 04 May 2020 18:40

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