Deep learning-based multi-criteria recommender system for technology-enhanced learning
Abstract Multi-Criteria Recommender Systems (MCRSs) improve personalization by incorporating multiple user preferences. However, their application in Technology-Enhanced Learning (TEL) remains limited due to challenges such as data sparsity, over-specialization, and cold-start problems. Traditional...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97407-3 |
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| author | Latifat Salau Hamada Mohamed Yunusa Simpa Abdulsalam Hassan Mohammed |
| author_facet | Latifat Salau Hamada Mohamed Yunusa Simpa Abdulsalam Hassan Mohammed |
| author_sort | Latifat Salau |
| collection | DOAJ |
| description | Abstract Multi-Criteria Recommender Systems (MCRSs) improve personalization by incorporating multiple user preferences. However, their application in Technology-Enhanced Learning (TEL) remains limited due to challenges such as data sparsity, over-specialization, and cold-start problems. Traditional techniques, such as Singular Value Decomposition (SVD) and SVD + + , struggle to effectively model the complex interactions within multi-criteria rating data, leading to suboptimal recommendations. This paper introduces a hybrid DeepFM-SVD + + model, which integrates deep learning and factorization-based techniques to improve multi-criteria recommendations. The model captures both low-order feature interactions using factorization machines and high-order dependencies through deep neural networks, enabling more adaptive recommendations. To evaluate its performance, the model is tested on two multi-criteria datasets: ITM-Rec (TEL domain) and Yahoo Movies (non-TEL domain). The experimental results show that DeepFM-SVD + + consistently outperforms the traditional techniques across multiple evaluation metrics. The findings highlight significant improvements in accuracy, demonstrating the model’s effectiveness in sparse datasets and its generalization across domains. By addressing the limitations of existing MCRS techniques, this study contributes to advancing personalized learning recommendations in TEL and expands the applicability of deep learning-based MCRS beyond educational contexts. |
| format | Article |
| id | doaj-art-91950c6f07af4c84a501ef6fecff52a6 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-91950c6f07af4c84a501ef6fecff52a62025-08-20T03:18:41ZengNature PortfolioScientific Reports2045-23222025-04-0115111710.1038/s41598-025-97407-3Deep learning-based multi-criteria recommender system for technology-enhanced learningLatifat Salau0Hamada Mohamed1Yunusa Simpa Abdulsalam2Hassan Mohammed3Department of Computer Science and Engineering, African University of Science and TechnologySoftware Engineering Lab, University of AizuCollege of Computing, University Mohammed VI PolytechnicDepartment of Software Engineering, Bayero UniversityAbstract Multi-Criteria Recommender Systems (MCRSs) improve personalization by incorporating multiple user preferences. However, their application in Technology-Enhanced Learning (TEL) remains limited due to challenges such as data sparsity, over-specialization, and cold-start problems. Traditional techniques, such as Singular Value Decomposition (SVD) and SVD + + , struggle to effectively model the complex interactions within multi-criteria rating data, leading to suboptimal recommendations. This paper introduces a hybrid DeepFM-SVD + + model, which integrates deep learning and factorization-based techniques to improve multi-criteria recommendations. The model captures both low-order feature interactions using factorization machines and high-order dependencies through deep neural networks, enabling more adaptive recommendations. To evaluate its performance, the model is tested on two multi-criteria datasets: ITM-Rec (TEL domain) and Yahoo Movies (non-TEL domain). The experimental results show that DeepFM-SVD + + consistently outperforms the traditional techniques across multiple evaluation metrics. The findings highlight significant improvements in accuracy, demonstrating the model’s effectiveness in sparse datasets and its generalization across domains. By addressing the limitations of existing MCRS techniques, this study contributes to advancing personalized learning recommendations in TEL and expands the applicability of deep learning-based MCRS beyond educational contexts.https://doi.org/10.1038/s41598-025-97407-3Multi-criteria recommender systemsTechnology-enhanced learningRecommender systemsDeep learning |
| spellingShingle | Latifat Salau Hamada Mohamed Yunusa Simpa Abdulsalam Hassan Mohammed Deep learning-based multi-criteria recommender system for technology-enhanced learning Scientific Reports Multi-criteria recommender systems Technology-enhanced learning Recommender systems Deep learning |
| title | Deep learning-based multi-criteria recommender system for technology-enhanced learning |
| title_full | Deep learning-based multi-criteria recommender system for technology-enhanced learning |
| title_fullStr | Deep learning-based multi-criteria recommender system for technology-enhanced learning |
| title_full_unstemmed | Deep learning-based multi-criteria recommender system for technology-enhanced learning |
| title_short | Deep learning-based multi-criteria recommender system for technology-enhanced learning |
| title_sort | deep learning based multi criteria recommender system for technology enhanced learning |
| topic | Multi-criteria recommender systems Technology-enhanced learning Recommender systems Deep learning |
| url | https://doi.org/10.1038/s41598-025-97407-3 |
| work_keys_str_mv | AT latifatsalau deeplearningbasedmulticriteriarecommendersystemfortechnologyenhancedlearning AT hamadamohamed deeplearningbasedmulticriteriarecommendersystemfortechnologyenhancedlearning AT yunusasimpaabdulsalam deeplearningbasedmulticriteriarecommendersystemfortechnologyenhancedlearning AT hassanmohammed deeplearningbasedmulticriteriarecommendersystemfortechnologyenhancedlearning |