คอลเล็กชัน (Collections)
พบ 2 รายการ
(ค้นหา: "bidirectional graph")
วารสารวิชาการ
ระดับนานาชาติ (SCOPUS) Q1
BiTG4Rec: Bidirectional Transformer Graphs for Sequential-Social Recommendation
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.
บทความประชุมวิชาการ
ระดับนานาชาติ
BiG: Bidirectional Graph Convolutional Networks for Social and Sequential Recommendation
Graph convolutional networks have obviously influenced recommender systems. However, we argue that previous research has limitations: 1) unidirectional approaches are not enough to explicitly capture the representation learning, 2) social influence is ignored to extract the user preference drifts, and 3) simple and crude fusion methods are not enough to seamlessly amalgamate the diverse behavioral perspective. To address these limitations, this research proposes a novelty of Bidirectional Graph convolutional networks for social and sequential recommendation (BiG). A novel graph convolutional networks are modified into bidirectional graphs to explicitly learn the representation of dynamic user preference at social level and sequential level. A novel Bidirectional Gate (BiGate) is designed to amalgamate the diverse preferences of social and sequential influences. Empirical experiment illustrates that BiG outperforms state-of-the-art methods.