Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey
This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems, addressing the lack of depth and precision in existing literature. It proposes a two-pronged approach: a thorough analysis of current algorithms and a novel, hierarchical taxonomy for precise catego...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020009 |
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| author | Kamal Taha Paul D. Yoo Chan Yeun Aya Taha |
| author_facet | Kamal Taha Paul D. Yoo Chan Yeun Aya Taha |
| author_sort | Kamal Taha |
| collection | DOAJ |
| description | This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems, addressing the lack of depth and precision in existing literature. It proposes a two-pronged approach: a thorough analysis of current algorithms and a novel, hierarchical taxonomy for precise categorization. The taxonomy is based on a tri-level hierarchy, starting with the methodology category and narrowing down to specific techniques. Such a framework allows for a structured and comprehensive classification of algorithms, assisting researchers in understanding the interrelationships among diverse algorithms and techniques. Covering a wide range of algorithms, this taxonomy first categorizes algorithms into four main analysis types: user and item similarity based methods, hybrid and combined approaches, deep learning and algorithmic methods, and mathematical modeling methods, with further subdivisions into sub-categories and techniques. The paper incorporates both empirical and experimental evaluations to differentiate between the techniques. The empirical evaluation ranks the techniques based on four criteria. The experimental assessments rank the algorithms that belong to the same category, sub-category, technique, and sub-technique. Also, the paper illuminates the future prospects of big data techniques in recommendation systems, underscoring potential advancements and opportunities for further research in this fields. |
| format | Article |
| id | doaj-art-3919b63a8115458eb9e9e8b58af4b39b |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-3919b63a8115458eb9e9e8b58af4b39b2025-02-03T00:12:56ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-09-0173964101410.26599/BDMA.2024.9020009Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive SurveyKamal Taha0Paul D. Yoo1Chan Yeun2Aya Taha3Department of Computer Science, Khalifa University, Abu Dhabi 127788, United Arab Emirates (UAE)School of Computing and Mathematical Sciences, Birkbeck College, University of London, London WC1E 7HU, UKDepartment of Computer Science, Khalifa University, Abu Dhabi 127788, United Arab Emirates (UAE)Department of Science, Brighton College, Dubai 122002, United Arab EmiratesThis survey paper provides a comprehensive analysis of big data algorithms in recommendation systems, addressing the lack of depth and precision in existing literature. It proposes a two-pronged approach: a thorough analysis of current algorithms and a novel, hierarchical taxonomy for precise categorization. The taxonomy is based on a tri-level hierarchy, starting with the methodology category and narrowing down to specific techniques. Such a framework allows for a structured and comprehensive classification of algorithms, assisting researchers in understanding the interrelationships among diverse algorithms and techniques. Covering a wide range of algorithms, this taxonomy first categorizes algorithms into four main analysis types: user and item similarity based methods, hybrid and combined approaches, deep learning and algorithmic methods, and mathematical modeling methods, with further subdivisions into sub-categories and techniques. The paper incorporates both empirical and experimental evaluations to differentiate between the techniques. The empirical evaluation ranks the techniques based on four criteria. The experimental assessments rank the algorithms that belong to the same category, sub-category, technique, and sub-technique. Also, the paper illuminates the future prospects of big data techniques in recommendation systems, underscoring potential advancements and opportunities for further research in this fields.https://www.sciopen.com/article/10.26599/BDMA.2024.9020009big data algorithmsrecommendation systemsrecommendation algorithmsdeep learning in recommendations |
| spellingShingle | Kamal Taha Paul D. Yoo Chan Yeun Aya Taha Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey Big Data Mining and Analytics big data algorithms recommendation systems recommendation algorithms deep learning in recommendations |
| title | Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey |
| title_full | Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey |
| title_fullStr | Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey |
| title_full_unstemmed | Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey |
| title_short | Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey |
| title_sort | empirical and experimental perspectives on big data in recommendation systems a comprehensive survey |
| topic | big data algorithms recommendation systems recommendation algorithms deep learning in recommendations |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020009 |
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