{"id":8668,"date":"2026-06-27T14:32:18","date_gmt":"2026-06-27T06:32:18","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=8668"},"modified":"2026-06-27T16:27:54","modified_gmt":"2026-06-27T08:27:54","slug":"jase-202610-33-016","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-016","title":{"rendered":"Dual-graph attention network-based Spatio-temporal multigranularity interest for English learning resources recommendation"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Huang Huang<\/i><\/a><\/p>\n\n\n\n

School of Foreign Languages, Zhengzhou University of Science and Technology, Zhengzhou China<\/p>\n<\/div>\n\n\n\n

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Received: April 14, 2026
Accepted: May 25, 2026
Publication Date: June 27, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Proposed DGAT-STMGI<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.016<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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With the proliferation of online English learning platforms, the information overload problem has become increasingly prominent, making personalized learning resource recommendation a critical research direction. Existing methods often fail to effectively model the complex topological correlations in English learning scenarios and cannot capture learners\u2019 spatio-temporal interest evolution at multiple granularities. To address these issues, this paper proposes a Dual-Graph Attention Network-based Spatio-Temporal Multi-Granularity Interest (DGAT-STMGI) model for English learning resources recommendation. The model constructs two heterogeneous graphs: a Learning Resource Semantic Graph (LRSG) depicting knowledge correlations among resources and a Learner Behavior Interaction Graph (LBIG) representing dynamic interaction patterns between learners and resources. We design a dual-graph attention mechanism to adaptively aggregate semantic and behavioral features, enabling fine-grained feature fusion. Furthermore, we propose a spatio-temporal multi granularity interest encoding module that captures short-term, medium-term, and long-term interest dynamics across temporal scales while integrating spatial topological dependencies. Extensive experiments on two real-world English learning datasets demonstrate that DGAT-STMGI significantly outperforms state-of-the-art recommendation methods in terms of Recall, NDCG, and MAP metrics, with maximum improvements of 8.73%, 7.62%, and 6.94% respectively, validating its effectiveness and superiority.<\/p>\n\n\n\n

Keywords: English learning resources recommendation; dual-graph attention network; spatio-temporal multi-granularity interest; heterogeneous graph neural network; personalized recommendation<\/em><\/p>\n\n\n\n

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