Online English teaching resource recommendation method design based on LightGCNCSCM
With the explosive growth of online English teaching resources, how to achieve personalized and high-quality resource recommendations has become a key issue that needs to be urgently solved. Existing methods have significant limitations in aspects such as cold start scenarios, semantic feature fusio...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941925001127 |
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| author | Jing Tang |
| author_facet | Jing Tang |
| author_sort | Jing Tang |
| collection | DOAJ |
| description | With the explosive growth of online English teaching resources, how to achieve personalized and high-quality resource recommendations has become a key issue that needs to be urgently solved. Existing methods have significant limitations in aspects such as cold start scenarios, semantic feature fusion, and the balance between computational efficiency and recommendation quality. The research proposes an online English teaching resource recommendation method. The local and global features of the user-resource interaction graph are captured through Lightweight graph convolutional networks, and the resource semantic vectors are extracted in combination with the content-based similarity calculation model. This can synergistically optimize behavior structure and content semantics. Experiment results show that this method significantly improves the recommendation quality in the cold start scenario. It balances the novelty of recommendation results and user preference matching through a dynamic weight allocation mechanism, while maintaining relatively low computational complexity. This method provides an efficient and robust personalized recommendation solution for online education platforms. |
| format | Article |
| id | doaj-art-80a353b8500e48ce86bb350e558d8fac |
| institution | OA Journals |
| issn | 2772-9419 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-80a353b8500e48ce86bb350e558d8fac2025-08-20T01:55:37ZengElsevierSystems and Soft Computing2772-94192025-12-01720029410.1016/j.sasc.2025.200294Online English teaching resource recommendation method design based on LightGCNCSCMJing Tang0College of General Education, Chongqing Vocational and Technical University of Mechatronics, Chongqing 401120, ChinaWith the explosive growth of online English teaching resources, how to achieve personalized and high-quality resource recommendations has become a key issue that needs to be urgently solved. Existing methods have significant limitations in aspects such as cold start scenarios, semantic feature fusion, and the balance between computational efficiency and recommendation quality. The research proposes an online English teaching resource recommendation method. The local and global features of the user-resource interaction graph are captured through Lightweight graph convolutional networks, and the resource semantic vectors are extracted in combination with the content-based similarity calculation model. This can synergistically optimize behavior structure and content semantics. Experiment results show that this method significantly improves the recommendation quality in the cold start scenario. It balances the novelty of recommendation results and user preference matching through a dynamic weight allocation mechanism, while maintaining relatively low computational complexity. This method provides an efficient and robust personalized recommendation solution for online education platforms.http://www.sciencedirect.com/science/article/pii/S2772941925001127Lightweight graph convolutional networksContent-based similarity calculation modelTeaching resourcesOnline educationRecommendation algorithm |
| spellingShingle | Jing Tang Online English teaching resource recommendation method design based on LightGCNCSCM Systems and Soft Computing Lightweight graph convolutional networks Content-based similarity calculation model Teaching resources Online education Recommendation algorithm |
| title | Online English teaching resource recommendation method design based on LightGCNCSCM |
| title_full | Online English teaching resource recommendation method design based on LightGCNCSCM |
| title_fullStr | Online English teaching resource recommendation method design based on LightGCNCSCM |
| title_full_unstemmed | Online English teaching resource recommendation method design based on LightGCNCSCM |
| title_short | Online English teaching resource recommendation method design based on LightGCNCSCM |
| title_sort | online english teaching resource recommendation method design based on lightgcncscm |
| topic | Lightweight graph convolutional networks Content-based similarity calculation model Teaching resources Online education Recommendation algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2772941925001127 |
| work_keys_str_mv | AT jingtang onlineenglishteachingresourcerecommendationmethoddesignbasedonlightgcncscm |