ES-Rank: evolution strategy learning to rank approach
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...
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| Format: | Conference or Workshop Item |
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2017
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| Online Access: | https://eprints.nottingham.ac.uk/41540/ |
| _version_ | 1848796298545725440 |
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| author | Ibrahim, Osman Ali Sadek Landa-Silva, Dario |
| author_facet | Ibrahim, Osman Ali Sadek Landa-Silva, Dario |
| author_sort | Ibrahim, Osman Ali Sadek |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:45:46Z |
| format | Conference or Workshop Item |
| id | nottingham-41540 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:45:46Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-415402020-05-04T18:40:54Z https://eprints.nottingham.ac.uk/41540/ ES-Rank: evolution strategy learning to rank approach Ibrahim, Osman Ali Sadek Landa-Silva, Dario 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. 2017-04-03 Conference or Workshop Item PeerReviewed 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. Learning to Rank; Evolution Strategy; Machine Learning; Information Retrieval http://dl.acm.org/citation.cfm?doid=3019612.3019696 |
| spellingShingle | Learning to Rank; Evolution Strategy; Machine Learning; Information Retrieval Ibrahim, Osman Ali Sadek Landa-Silva, Dario ES-Rank: evolution strategy learning to rank approach |
| title | ES-Rank: evolution strategy learning to rank approach |
| title_full | ES-Rank: evolution strategy learning to rank approach |
| title_fullStr | ES-Rank: evolution strategy learning to rank approach |
| title_full_unstemmed | ES-Rank: evolution strategy learning to rank approach |
| title_short | ES-Rank: evolution strategy learning to rank approach |
| title_sort | es-rank: evolution strategy learning to rank approach |
| topic | Learning to Rank; Evolution Strategy; Machine Learning; Information Retrieval |
| url | https://eprints.nottingham.ac.uk/41540/ https://eprints.nottingham.ac.uk/41540/ |