Deep Learning-Based Recommendation System: Systematic Review and Classification
In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10274963/ |
| _version_ | 1797655642791477248 |
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| author | Caiwen Li Iskandar Ishak Hamidah Ibrahim Maslina Zolkepli Fatimah Sidi Caili Li |
| author_facet | Caiwen Li Iskandar Ishak Hamidah Ibrahim Maslina Zolkepli Fatimah Sidi Caili Li |
| author_sort | Caiwen Li |
| collection | DOAJ |
| description | In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature regarding classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains. To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification. We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems. |
| first_indexed | 2024-03-11T17:17:26Z |
| format | Article |
| id | doaj.art-5e1e700071034be6852dea5d8d316c78 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T17:17:26Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5e1e700071034be6852dea5d8d316c782023-10-19T23:01:29ZengIEEEIEEE Access2169-35362023-01-011111379011383510.1109/ACCESS.2023.332335310274963Deep Learning-Based Recommendation System: Systematic Review and ClassificationCaiwen Li0https://orcid.org/0009-0006-8341-965XIskandar Ishak1https://orcid.org/0000-0001-8874-1417Hamidah Ibrahim2https://orcid.org/0000-0002-9900-0531Maslina Zolkepli3Fatimah Sidi4https://orcid.org/0000-0001-9556-9045Caili Li5Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Serdang, Selangor, MalaysiaDepartment of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Serdang, Selangor, MalaysiaDepartment of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Serdang, Selangor, MalaysiaDepartment of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Serdang, Selangor, MalaysiaDepartment of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Serdang, Selangor, MalaysiaCollege of Art and Design, Heilongjiang Institute of Technology, Daowai, Harbin, ChinaIn recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature regarding classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains. To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification. We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems.https://ieeexplore.ieee.org/document/10274963/Deep learningterm classificationrecommendation systemsystematic reviewstate-of-the-art techniques |
| spellingShingle | Caiwen Li Iskandar Ishak Hamidah Ibrahim Maslina Zolkepli Fatimah Sidi Caili Li Deep Learning-Based Recommendation System: Systematic Review and Classification IEEE Access Deep learning term classification recommendation system systematic review state-of-the-art techniques |
| title | Deep Learning-Based Recommendation System: Systematic Review and Classification |
| title_full | Deep Learning-Based Recommendation System: Systematic Review and Classification |
| title_fullStr | Deep Learning-Based Recommendation System: Systematic Review and Classification |
| title_full_unstemmed | Deep Learning-Based Recommendation System: Systematic Review and Classification |
| title_short | Deep Learning-Based Recommendation System: Systematic Review and Classification |
| title_sort | deep learning based recommendation system systematic review and classification |
| topic | Deep learning term classification recommendation system systematic review state-of-the-art techniques |
| url | https://ieeexplore.ieee.org/document/10274963/ |
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