An evolutionary strategy with machine learning for learning to rank in information retrieval
Learning to Rank (LTR) is one of the problems in Information Retrieval (IR) that nowadays attracts attention from researchers. The LTR problem refers to ranking the retrieved documents for users in search engines, question answering and product recommendation systems. There is a number of LTR approa...
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| Format: | Article |
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Springer
2018
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| Online Access: | https://eprints.nottingham.ac.uk/48943/ |
| _version_ | 1848797885432332288 |
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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 problems in Information Retrieval (IR) that nowadays attracts attention from researchers. The LTR problem refers to ranking the retrieved documents for users in search engines, question answering and product recommendation systems. There is a number of LTR approaches based on machine learning and computational intelligence techniques. Most existing LTR methods have limitations, like being too slow or not being very effective or requiring large computer memory to operate. This paper proposes a LTR method that combines a (1+1)-Evolutionary Strategy with machine learning. Three variants of the method are investigated: ES-Rank, IESR-Rank and IESVMRank. They differ on the mechanism to initialize the chromosome for the evolutionary process. ES-Rank simply sets all genes in the initial chromosome to the same value. IESRRank uses linear regression and IESVM-Rank uses support vector machine for the initialization process. Experimental results from comparing the proposed method to fourteen other approaches from the literature show that IESRRank achieves the overall best performance. Ten problem instances are used here, obtained from four datasets: MSLR-WEB10K, LETOR 3 and LETOR 4. Performance is measured at the top-10 query-document pairs retrieved, using five metrics: Mean Average Precision (MAP), Root Mean Square Error (RMSE), Precision (P@10), Reciprocal Rank (RR@10) and Normalized Discounted Cumulative Gain (NDCG@10). The contribution of this paper is an effective and efficient LTR method combining a listwise evolutionary technique with point-wise and pair-wise machine learning techniques. |
| first_indexed | 2025-11-14T20:10:59Z |
| format | Article |
| id | nottingham-48943 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:10:59Z |
| publishDate | 2018 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-489432020-05-04T19:35:19Z https://eprints.nottingham.ac.uk/48943/ An evolutionary strategy with machine learning for learning to rank in information retrieval Ibrahim, Osman Ali Sadek Landa-Silva, Dario Learning to Rank (LTR) is one of the problems in Information Retrieval (IR) that nowadays attracts attention from researchers. The LTR problem refers to ranking the retrieved documents for users in search engines, question answering and product recommendation systems. There is a number of LTR approaches based on machine learning and computational intelligence techniques. Most existing LTR methods have limitations, like being too slow or not being very effective or requiring large computer memory to operate. This paper proposes a LTR method that combines a (1+1)-Evolutionary Strategy with machine learning. Three variants of the method are investigated: ES-Rank, IESR-Rank and IESVMRank. They differ on the mechanism to initialize the chromosome for the evolutionary process. ES-Rank simply sets all genes in the initial chromosome to the same value. IESRRank uses linear regression and IESVM-Rank uses support vector machine for the initialization process. Experimental results from comparing the proposed method to fourteen other approaches from the literature show that IESRRank achieves the overall best performance. Ten problem instances are used here, obtained from four datasets: MSLR-WEB10K, LETOR 3 and LETOR 4. Performance is measured at the top-10 query-document pairs retrieved, using five metrics: Mean Average Precision (MAP), Root Mean Square Error (RMSE), Precision (P@10), Reciprocal Rank (RR@10) and Normalized Discounted Cumulative Gain (NDCG@10). The contribution of this paper is an effective and efficient LTR method combining a listwise evolutionary technique with point-wise and pair-wise machine learning techniques. Springer 2018-05-01 Article PeerReviewed Ibrahim, Osman Ali Sadek and Landa-Silva, Dario (2018) An evolutionary strategy with machine learning for learning to rank in information retrieval. Soft Computing, 22 (10). pp. 3171-3185. ISSN 1432-7643 Learning to rank ; Evolution strategy ; Linear regression ; Support vector machine https://link.springer.com/article/10.1007%2Fs00500-017-2988-6 doi:10.1007/s00500-017-2988-6 doi:10.1007/s00500-017-2988-6 |
| spellingShingle | Learning to rank ; Evolution strategy ; Linear regression ; Support vector machine Ibrahim, Osman Ali Sadek Landa-Silva, Dario An evolutionary strategy with machine learning for learning to rank in information retrieval |
| title | An evolutionary strategy with machine learning for learning to rank in information retrieval |
| title_full | An evolutionary strategy with machine learning for learning to rank in information retrieval |
| title_fullStr | An evolutionary strategy with machine learning for learning to rank in information retrieval |
| title_full_unstemmed | An evolutionary strategy with machine learning for learning to rank in information retrieval |
| title_short | An evolutionary strategy with machine learning for learning to rank in information retrieval |
| title_sort | evolutionary strategy with machine learning for learning to rank in information retrieval |
| topic | Learning to rank ; Evolution strategy ; Linear regression ; Support vector machine |
| url | https://eprints.nottingham.ac.uk/48943/ https://eprints.nottingham.ac.uk/48943/ https://eprints.nottingham.ac.uk/48943/ |