Research on Personalized Course Resource Recommendation Method Based on GEMRec
With the rapid growth of online educational resources, existing personalized course recommendation systems face challenges in multimodal feature integration and limited recommendation interpretability when dealing with complex and diverse instructional content. This paper proposes a graph-enhanced m...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/3/1075 |
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| author | Enliang Wang Zhixin Sun |
| author_facet | Enliang Wang Zhixin Sun |
| author_sort | Enliang Wang |
| collection | DOAJ |
| description | With the rapid growth of online educational resources, existing personalized course recommendation systems face challenges in multimodal feature integration and limited recommendation interpretability when dealing with complex and diverse instructional content. This paper proposes a graph-enhanced multimodal recommendation method (GEMRec), which effectively integrates text, video, and audio features through a graph attention network and differentiable pooling. Innovatively, GEMRec introduces graph edit distance into the recommendation system to measure the structural similarity between a learner’s knowledge state and course content at the knowledge graph level. Additionally, it combines SHAP (SHapley Additive exPlanations) value computation with large language models to generate reliable and personalized recommendation explanations. Experiments on the MOOCCubeX dataset demonstrate that the GEMRec model exhibits strong convergence and generalization during training. Compared with existing methods, GEMRec achieves 0.267, 0.265, and 0.297 on the Precision@10, Recall@10, and NDCG@10 metrics, respectively, significantly outperforming traditional collaborative filtering and other deep learning models. These results validate the effectiveness of multimodal feature integration and knowledge graph enhancement in improving recommendation performance. |
| format | Article |
| id | doaj-art-7f583a70d697473c883a2ebcf30cead6 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7f583a70d697473c883a2ebcf30cead62025-08-20T03:12:34ZengMDPI AGApplied Sciences2076-34172025-01-01153107510.3390/app15031075Research on Personalized Course Resource Recommendation Method Based on GEMRecEnliang Wang0Zhixin Sun1Post Big Data Technology and Application Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaPost Big Data Technology and Application Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaWith the rapid growth of online educational resources, existing personalized course recommendation systems face challenges in multimodal feature integration and limited recommendation interpretability when dealing with complex and diverse instructional content. This paper proposes a graph-enhanced multimodal recommendation method (GEMRec), which effectively integrates text, video, and audio features through a graph attention network and differentiable pooling. Innovatively, GEMRec introduces graph edit distance into the recommendation system to measure the structural similarity between a learner’s knowledge state and course content at the knowledge graph level. Additionally, it combines SHAP (SHapley Additive exPlanations) value computation with large language models to generate reliable and personalized recommendation explanations. Experiments on the MOOCCubeX dataset demonstrate that the GEMRec model exhibits strong convergence and generalization during training. Compared with existing methods, GEMRec achieves 0.267, 0.265, and 0.297 on the Precision@10, Recall@10, and NDCG@10 metrics, respectively, significantly outperforming traditional collaborative filtering and other deep learning models. These results validate the effectiveness of multimodal feature integration and knowledge graph enhancement in improving recommendation performance.https://www.mdpi.com/2076-3417/15/3/1075multimodal deep learningknowledge graphpersonalized course recommendationgraph neural networkinterpretability |
| spellingShingle | Enliang Wang Zhixin Sun Research on Personalized Course Resource Recommendation Method Based on GEMRec Applied Sciences multimodal deep learning knowledge graph personalized course recommendation graph neural network interpretability |
| title | Research on Personalized Course Resource Recommendation Method Based on GEMRec |
| title_full | Research on Personalized Course Resource Recommendation Method Based on GEMRec |
| title_fullStr | Research on Personalized Course Resource Recommendation Method Based on GEMRec |
| title_full_unstemmed | Research on Personalized Course Resource Recommendation Method Based on GEMRec |
| title_short | Research on Personalized Course Resource Recommendation Method Based on GEMRec |
| title_sort | research on personalized course resource recommendation method based on gemrec |
| topic | multimodal deep learning knowledge graph personalized course recommendation graph neural network interpretability |
| url | https://www.mdpi.com/2076-3417/15/3/1075 |
| work_keys_str_mv | AT enliangwang researchonpersonalizedcourseresourcerecommendationmethodbasedongemrec AT zhixinsun researchonpersonalizedcourseresourcerecommendationmethodbasedongemrec |