{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:28:40Z","timestamp":1771003720244,"version":"3.50.1"},"reference-count":30,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T00:00:00Z","timestamp":1740614400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p>This paper introduces an innovative online resource recommendation system tailored for English text and video content, leveraging the power of attention mechanisms and graph neural networks. Given the exponential growth of online learning resources, a crucial challenge lies in delivering personalized and efficient recommendations to users. Our study strives to optimize both the accuracy and efficiency of these recommendations by harnessing the synergistic effects of attention mechanisms and GNNs. By collecting and analyzing a large amount of user behavior data, we build a user-resource interaction graph. This graph not only contains the interaction information between users and resources, but also incorporates the association information between resources, providing a rich context for subsequent recommendations. We introduce an attention mechanism to handle node and edge information in graphs. By assessing the significance of various nodes and edges in the recommendation process, we are able to capture users\u2019 interests and preferences with greater precision. According to experimental data, the integration of an attention mechanism has led to a notable improvement in the system\u2019s recommendation accuracy, achieving an increase of approximately 15%. This significant enhancement underscores the effectiveness of the attention mechanism in effectively capturing user interests. Additionally, we leverage graph neural networks to model the intricate structural information within the graph. With graph convolution operations, we are able to capture potential relationships between resources and use these relationships in the recommendation process. Experimental results show that combined with GNN, the recommendation coverage of the system has increased by about 20%, providing users with more diverse recommendation results. The proposed online resource recommendation system for English text and video based on attention mechanism and GNN has achieved significant improvements in both accuracy and diversity of recommendations. In the future, we will further explore more optimization methods to provide more personalized and efficient online learning resource recommendation services.<\/jats:p>","DOI":"10.1177\/14727978251322545","type":"journal-article","created":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T17:41:16Z","timestamp":1740678076000},"page":"3593-3607","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["English text and video online resource recommendation based on attention mechanism and GNN"],"prefix":"10.1177","volume":"25","author":[{"given":"Zunlan","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Foreign Studies, Shaoyang University, Shaoyang, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8745-7647","authenticated-orcid":false,"given":"Zhihao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Foreign Studies, Shaoyang University, Shaoyang, China"}]},{"given":"Yin","family":"Li","sequence":"additional","affiliation":[{"name":"Shaoyang University, Shaoyang, China"}]},{"given":"Zuyan","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Foreign Studies, Shaoyang University, Shaoyang, China"}]}],"member":"179","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2024.118415"},{"key":"e_1_3_3_3_2","first-page":"117155","article-title":"Line segment detectors with deformable attention","volume":"128","author":"Tang S","year":"2024","unstructured":"Tang S, Zhou S, Tong X, et al. 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