Explainable AI and deep learning models for recommender systems: State of the art and challenges
Recommender systems have a pivotal function in delivering customized and pertinent suggestions to clients on the basis of their preferences and activities. The present paper presents a thorough overview of deep learning-based recommender systems, explores their application to enhance performance, an...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Graz University of Technology
2025-03-01
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| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/122380/download/pdf/ |
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| _version_ | 1850064105392570368 |
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| author | Maroua Benleulmi Ibtissem Gasmi Nabiha Azizi Nilanjan Dey |
| author_facet | Maroua Benleulmi Ibtissem Gasmi Nabiha Azizi Nilanjan Dey |
| author_sort | Maroua Benleulmi |
| collection | DOAJ |
| description | Recommender systems have a pivotal function in delivering customized and pertinent suggestions to clients on the basis of their preferences and activities. The present paper presents a thorough overview of deep learning-based recommender systems, explores their application to enhance performance, and overcomes limitations. The survey encompasses fundamental models of recommender systems; moreover, it also delves into key deep learning models. This discussion focuses on the effective integration of deep learning techniques into recommender systems. Real-world applications highlight the effectiveness of these approaches in capturing complex and nonlinear patterns from large-scale data. This paper concludes by reflecting on challenges encountered in this research field and outlines potential future directions, offering valuable insights for academics and professionals in the field of recommender systems based on deep learning.  |
| format | Article |
| id | doaj-art-48d09aed79054330a90e4517432c57b3 |
| institution | DOAJ |
| issn | 0948-6968 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj-art-48d09aed79054330a90e4517432c57b32025-08-20T02:49:23ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682025-03-0131438342110.3897/jucs.122380122380Explainable AI and deep learning models for recommender systems: State of the art and challengesMaroua Benleulmi0Ibtissem Gasmi1Nabiha Azizi2Nilanjan Dey3Chadli Bendjedid El Tarf UniversityChadli Bendjedid El Tarf UniversityBadji Mokhtar Annaba UniversityTechno International New TownRecommender systems have a pivotal function in delivering customized and pertinent suggestions to clients on the basis of their preferences and activities. The present paper presents a thorough overview of deep learning-based recommender systems, explores their application to enhance performance, and overcomes limitations. The survey encompasses fundamental models of recommender systems; moreover, it also delves into key deep learning models. This discussion focuses on the effective integration of deep learning techniques into recommender systems. Real-world applications highlight the effectiveness of these approaches in capturing complex and nonlinear patterns from large-scale data. This paper concludes by reflecting on challenges encountered in this research field and outlines potential future directions, offering valuable insights for academics and professionals in the field of recommender systems based on deep learning. https://lib.jucs.org/article/122380/download/pdf/Recommender systemsdeep learningXAIbenchmark |
| spellingShingle | Maroua Benleulmi Ibtissem Gasmi Nabiha Azizi Nilanjan Dey Explainable AI and deep learning models for recommender systems: State of the art and challenges Journal of Universal Computer Science Recommender systems deep learning XAI benchmark |
| title | Explainable AI and deep learning models for recommender systems: State of the art and challenges |
| title_full | Explainable AI and deep learning models for recommender systems: State of the art and challenges |
| title_fullStr | Explainable AI and deep learning models for recommender systems: State of the art and challenges |
| title_full_unstemmed | Explainable AI and deep learning models for recommender systems: State of the art and challenges |
| title_short | Explainable AI and deep learning models for recommender systems: State of the art and challenges |
| title_sort | explainable ai and deep learning models for recommender amp nbsp systems state of the art and challenges |
| topic | Recommender systems deep learning XAI benchmark |
| url | https://lib.jucs.org/article/122380/download/pdf/ |
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