Temporal-based approach to solve item decay problem in recommendation system
The rating matrix of a recommendation system contains a high percentage of data sparsity which lowers the prediction accuracy of the collaborative filtering technique (CF). Recently, the temporal based factorization approaches have been used to solve the sparsity problem, but these approaches have a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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American Scientific Publishers
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/64654/ http://psasir.upm.edu.my/id/eprint/64654/1/Temporal-based%20approach%20to%20solve%20item%20decay%20problem%20in%20recommendation%20system.pdf |
| _version_ | 1848855060088356864 |
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| author | Al-Qasem, Al-Hadi Ismail Ahmed Mohd Sharef, Nurfadhlina Sulaiman, Md. Nasir Mustapha, Norwati |
| author_facet | Al-Qasem, Al-Hadi Ismail Ahmed Mohd Sharef, Nurfadhlina Sulaiman, Md. Nasir Mustapha, Norwati |
| author_sort | Al-Qasem, Al-Hadi Ismail Ahmed |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The rating matrix of a recommendation system contains a high percentage of data sparsity which lowers the prediction accuracy of the collaborative filtering technique (CF). Recently, the temporal based factorization approaches have been used to solve the sparsity problem, but these approaches have a weakness in terms of learning the popularity decay of items during the long-term which lowers the prediction accuracy of the CF technique. The LongTemporalMF approach has been proposed to solve these problems. The x-means algorithm and the bacterial foraging optimization algorithm have been integrated within the LongTemporalMF approach to generate and optimize the genres weights which are integrated with the factorization features and the long-term preferences in terms of personality. The experimental results show that the LongTemporalMF approach has the accurate prediction performance compared to the benchmark approaches. |
| first_indexed | 2025-11-15T11:19:45Z |
| format | Article |
| id | upm-64654 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:19:45Z |
| publishDate | 2018 |
| publisher | American Scientific Publishers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-646542018-08-13T03:16:17Z http://psasir.upm.edu.my/id/eprint/64654/ Temporal-based approach to solve item decay problem in recommendation system Al-Qasem, Al-Hadi Ismail Ahmed Mohd Sharef, Nurfadhlina Sulaiman, Md. Nasir Mustapha, Norwati The rating matrix of a recommendation system contains a high percentage of data sparsity which lowers the prediction accuracy of the collaborative filtering technique (CF). Recently, the temporal based factorization approaches have been used to solve the sparsity problem, but these approaches have a weakness in terms of learning the popularity decay of items during the long-term which lowers the prediction accuracy of the CF technique. The LongTemporalMF approach has been proposed to solve these problems. The x-means algorithm and the bacterial foraging optimization algorithm have been integrated within the LongTemporalMF approach to generate and optimize the genres weights which are integrated with the factorization features and the long-term preferences in terms of personality. The experimental results show that the LongTemporalMF approach has the accurate prediction performance compared to the benchmark approaches. American Scientific Publishers 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/64654/1/Temporal-based%20approach%20to%20solve%20item%20decay%20problem%20in%20recommendation%20system.pdf Al-Qasem, Al-Hadi Ismail Ahmed and Mohd Sharef, Nurfadhlina and Sulaiman, Md. Nasir and Mustapha, Norwati (2018) Temporal-based approach to solve item decay problem in recommendation system. Advanced Science Letters, 24 (2). pp. 1421-1426. ISSN 1936-6612; ESSN: 1936-7317 https://www.ingentaconnect.com/contentone/asp/asl/2018/00000024/00000002/art00136 10.1166/asl.2018.10762 |
| spellingShingle | Al-Qasem, Al-Hadi Ismail Ahmed Mohd Sharef, Nurfadhlina Sulaiman, Md. Nasir Mustapha, Norwati Temporal-based approach to solve item decay problem in recommendation system |
| title | Temporal-based approach to solve item decay problem in recommendation system |
| title_full | Temporal-based approach to solve item decay problem in recommendation system |
| title_fullStr | Temporal-based approach to solve item decay problem in recommendation system |
| title_full_unstemmed | Temporal-based approach to solve item decay problem in recommendation system |
| title_short | Temporal-based approach to solve item decay problem in recommendation system |
| title_sort | temporal-based approach to solve item decay problem in recommendation system |
| url | http://psasir.upm.edu.my/id/eprint/64654/ http://psasir.upm.edu.my/id/eprint/64654/ http://psasir.upm.edu.my/id/eprint/64654/ http://psasir.upm.edu.my/id/eprint/64654/1/Temporal-based%20approach%20to%20solve%20item%20decay%20problem%20in%20recommendation%20system.pdf |