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ผศ.ดร.นิกร กรรณิกากลาง | Bidirectional methods graph convolutional network graph attention network

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Collection	RMUTK Research Repository (RMUTK IR) วารสารวิชาการ — e-Journal Articles
ID RMUTK Digital	RMUTK000022
Title
Alternative title	BiTG4Rec: Bidirectional Transformer Graphs for Sequential-Social Recommendation
Authors	ผศ.ดร.นิกร กรรณิกากลาง ผู้แต่งหลัก
Faculty	คณะบริหารธุรกิจ
Journal Title	IEEE Access
ISSN	2169-3536
Volume / Issue / Pages	Vol.13 \| No.- \| pp.175190 - 175211
Published	2025-09-30
Year	2568
Level	ระดับนานาชาติ (SCOPUS)
Quartile	Q1
DOI	10.1109/ACCESS.2025.3616206
Abstract	Sequential-social recommendation systems are essential for understanding users’ evolving interests and predicting their future behaviors. While existing methods employing bidirectional graph modeling have shown promising results in capturing user interactions, they face significant challenges in effectively leveraging bidirectional information and addressing dynamic user preferences. To overcome these limitations, we propose Bidirectional Transformer Graphs for Sequential-Social Recommendation (BiTG4Rec), a novel framework that integrates both supervised and self-supervised learning to analyze dynamic user preferences at sequential and social levels through bidirectional graphs. Our approach comprises three core components: 1) a Bidirectional Dynamic Graph Convolutional Network (BiDGCN) that models long-term sequential preferences, 2) a Bidirectional Dynamic Graph Attention Network (BiDGAT) that captures short-term sequential preferences, and 3) a Bidirectional Hyperbolic Graph Contrastive Learning module (BiHGCL) that extracts social preferences. To unify these diverse signals, we introduce a TransformerGated mechanism that dynamically integrates long-term, short-term, and social preferences. Extensive experiments on multiple real-world datasets demonstrate that BiTG4Rec consistently outperforms state-of-the-art methods, validating its effectiveness for sequential-social recommendation tasks.
Abstract (EN)	Sequential-social recommendation systems are essential for understanding users’ evolving interests and predicting their future behaviors. While existing methods employing bidirectional graph modeling have shown promising results in capturing user interactions, they face significant challenges in effectively leveraging bidirectional information and addressing dynamic user preferences. To overcome these limitations, we propose Bidirectional Transformer Graphs for Sequential-Social Recommendation (BiTG4Rec), a novel framework that integrates both supervised and self-supervised learning to analyze dynamic user preferences at sequential and social levels through bidirectional graphs. Our approach comprises three core components: 1) a Bidirectional Dynamic Graph Convolutional Network (BiDGCN) that models long-term sequential preferences, 2) a Bidirectional Dynamic Graph Attention Network (BiDGAT) that captures short-term sequential preferences, and 3) a Bidirectional Hyperbolic Graph Contrastive Learning module (BiHGCL) that extracts social preferences. To unify these diverse signals, we introduce a TransformerGated mechanism that dynamically integrates long-term, short-term, and social preferences. Extensive experiments on multiple real-world datasets demonstrate that BiTG4Rec consistently outperforms state-of-the-art methods, validating its effectiveness for sequential-social recommendation tasks.
Keywords (TH)	Bidirectional methods graph convolutional network graph attention network graph contrastive learning sequential recommendation social recommendation transformer
Keywords (EN)	Bidirectional methods graph convolutional network graph attention network graph contrastive learning sequential recommendation social recommendation transformer
Access Level	Open Access (เปิดสาธารณะ)

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https://ieeexplore.ieee.org/document/11184816

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