Review of the temporal recommendation system with matrix factorization
The temporal recommendation system (TRS) is designed for providing users with an accurate prediction based on the history of their behaviour during a precise time. Most TRS approaches use matrix factorization and collaborative filtering, which are primarily based on the distribution of the user pref...
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| Format: | Article |
| Language: | English |
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ICIC International
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/62987/1/Review%20of%20the%20temporal%20recommendation%20system%20with%20matrix%20factorization.pdf |
| _version_ | 1825932543448317952 |
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| author | Al-Hadi, Ismail Ahmed Al-Qasem Mohd Sharef, Nurfadhlina Sulaiman, Md. Nasir Mustapha, Norwati |
| author_facet | Al-Hadi, Ismail Ahmed Al-Qasem Mohd Sharef, Nurfadhlina Sulaiman, Md. Nasir Mustapha, Norwati |
| author_sort | Al-Hadi, Ismail Ahmed Al-Qasem |
| collection | UPM |
| description | The temporal recommendation system (TRS) is designed for providing users with an accurate prediction based on the history of their behaviour during a precise time. Most TRS approaches use matrix factorization and collaborative filtering, which are primarily based on the distribution of the user preferences. Recently, TRS has gained significant attention because it improves the accuracy of prediction. This is because since the temporal drift in the user preferences is observed, users' preferences within the short term and long term can be utilized to predict the best item to be recommended. Several existing review papers have focused on the general problems of the recommendation system (RS) and similarity measures, and they refer to recent improvements based on three recommendation strategies which are user rating, tagging and trust values. However, there is a lack of recent review papers of TRS with rating score strategy, especially in terms of learning factorization features of temporal terms. This paper fills this gap and highlights the issues and challenges for both general and temporal RS techniques. The prediction approaches based on collaborative filtering technique are reviewed depending on the behaviour of users and items. The challenges and approaches of temporal-based RS are discussed. This review includes the matrix factorization approaches that are integrated with such temporal factors as long-term preferences, short-term preferences, decay, and drift. The outcome of this review prioritizes guidance to focus on matrix factorization, temporal terms and drifting of users' preferences. |
| first_indexed | 2024-03-06T09:43:39Z |
| format | Article |
| id | upm.eprints-62987 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:43:39Z |
| publishDate | 2017 |
| publisher | ICIC International |
| record_format | dspace |
| spelling | upm.eprints-629872018-09-28T10:26:47Z http://psasir.upm.edu.my/id/eprint/62987/ Review of the temporal recommendation system with matrix factorization Al-Hadi, Ismail Ahmed Al-Qasem Mohd Sharef, Nurfadhlina Sulaiman, Md. Nasir Mustapha, Norwati The temporal recommendation system (TRS) is designed for providing users with an accurate prediction based on the history of their behaviour during a precise time. Most TRS approaches use matrix factorization and collaborative filtering, which are primarily based on the distribution of the user preferences. Recently, TRS has gained significant attention because it improves the accuracy of prediction. This is because since the temporal drift in the user preferences is observed, users' preferences within the short term and long term can be utilized to predict the best item to be recommended. Several existing review papers have focused on the general problems of the recommendation system (RS) and similarity measures, and they refer to recent improvements based on three recommendation strategies which are user rating, tagging and trust values. However, there is a lack of recent review papers of TRS with rating score strategy, especially in terms of learning factorization features of temporal terms. This paper fills this gap and highlights the issues and challenges for both general and temporal RS techniques. The prediction approaches based on collaborative filtering technique are reviewed depending on the behaviour of users and items. The challenges and approaches of temporal-based RS are discussed. This review includes the matrix factorization approaches that are integrated with such temporal factors as long-term preferences, short-term preferences, decay, and drift. The outcome of this review prioritizes guidance to focus on matrix factorization, temporal terms and drifting of users' preferences. ICIC International 2017-10 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/62987/1/Review%20of%20the%20temporal%20recommendation%20system%20with%20matrix%20factorization.pdf Al-Hadi, Ismail Ahmed Al-Qasem and Mohd Sharef, Nurfadhlina and Sulaiman, Md. Nasir and Mustapha, Norwati (2017) Review of the temporal recommendation system with matrix factorization. International Journal of Innovative Computing, Information and Control, 13 (5). 1579 - 1594. ISSN 1349-4198 http://www.ijicic.org/ijicic-130511.pdf |
| spellingShingle | Al-Hadi, Ismail Ahmed Al-Qasem Mohd Sharef, Nurfadhlina Sulaiman, Md. Nasir Mustapha, Norwati Review of the temporal recommendation system with matrix factorization |
| title | Review of the temporal recommendation system with matrix factorization |
| title_full | Review of the temporal recommendation system with matrix factorization |
| title_fullStr | Review of the temporal recommendation system with matrix factorization |
| title_full_unstemmed | Review of the temporal recommendation system with matrix factorization |
| title_short | Review of the temporal recommendation system with matrix factorization |
| title_sort | review of the temporal recommendation system with matrix factorization |
| url | http://psasir.upm.edu.my/id/eprint/62987/1/Review%20of%20the%20temporal%20recommendation%20system%20with%20matrix%20factorization.pdf |
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