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ผศ.ดร.นิกร กรรณิกากลาง | Graph contrastive learning graph isomorphism network attention network

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Collection	RMUTK Research Repository (RMUTK IR) วารสารวิชาการ — e-Journal Articles
ID RMUTK Digital	RMUTK000023
Title
Alternative title	GCA4Rec: Graph-Based Co-Attention Networks for Sequential and Social Recommendation
Authors	ผศ.ดร.นิกร กรรณิกากลาง ผู้แต่งหลัก
Faculty	คณะบริหารธุรกิจ
Journal Title	IEEE Access
ISSN	2169-3536
Volume / Issue / Pages	Vol.13 \| No.- \| pp.179090 - 179111
Published	2025-10-14
Year	2568
Level	ระดับนานาชาติ (SCOPUS)
Quartile	Q1
DOI	10.1109/ACCESS.2025.3621313
Abstract	The integration of sequential and social recommendations using a graph-based co-attention architecture marks a significant advancement in deep learning, substantially improving recommender system performance. This innovation enables effective representation learning of user interactions by capturing dynamic preferences influenced by both sequential and social contexts. While prior research has attempted to model evolving user preferences, existing approaches suffer from two key limitations: 1) ineffective modeling of complex sequential dependencies and 2) overlooking the hierarchical nature of social influence, resulting in suboptimal performance. To address these challenges, we propose GCA4Rec (Graph-Based Co-Attention Networks for Sequential and Social Recommendation), a novel framework designed to handle the complexities of user preference dynamics across sequential and social domains. Our model combines a graph attention contrastive learning module to track sequential preference shifts, while a hyperbolic graph attention isomorphism network models social-level preference dynamics. Additionally, we introduce fusionGated, a novel gating mechanism that effectively integrates co-attention signals from both levels. Extensive experiments on real-world benchmark datasets demonstrate that GCA4Rec outperforms state-of-the-art methods in Top-k recommendation tasks and exhibits robustness under varying degrees of data sparsity.
Abstract (EN)	The integration of sequential and social recommendations using a graph-based co-attention architecture marks a significant advancement in deep learning, substantially improving recommender system performance. This innovation enables effective representation learning of user interactions by capturing dynamic preferences influenced by both sequential and social contexts. While prior research has attempted to model evolving user preferences, existing approaches suffer from two key limitations: 1) ineffective modeling of complex sequential dependencies and 2) overlooking the hierarchical nature of social influence, resulting in suboptimal performance. To address these challenges, we propose GCA4Rec (Graph-Based Co-Attention Networks for Sequential and Social Recommendation), a novel framework designed to handle the complexities of user preference dynamics across sequential and social domains. Our model combines a graph attention contrastive learning module to track sequential preference shifts, while a hyperbolic graph attention isomorphism network models social-level preference dynamics. Additionally, we introduce fusionGated, a novel gating mechanism that effectively integrates co-attention signals from both levels. Extensive experiments on real-world benchmark datasets demonstrate that GCA4Rec outperforms state-of-the-art methods in Top-k recommendation tasks and exhibits robustness under varying degrees of data sparsity.
Keywords (TH)	Graph contrastive learning graph isomorphism network attention network sequential recommendation social recommendation
Keywords (EN)	Graph contrastive learning graph isomorphism network attention network sequential recommendation social recommendation
Access Level	Open Access (เปิดสาธารณะ)

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https://ieeexplore.ieee.org/document/11202868

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