Session-based recommendation with hypergraph convolutional networks and sequential information embeddings

Ding, Chengxin; Zhao, Zhongying; Li, Chao; Yu, Yanwei; Zeng, Qingtian

doi:10.1016/j.eswa.2023.119875

Cited by 12 publications

(1 citation statement)

References 43 publications

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“…There are also a few studies that integrate hypergraph learning with prediction tasks. For instance, Ding et al [19] proposed learning two types of project embeddings based on hypergraph convolutional networks and gated recurrent units. They flexibly combined these two embeddings using an attention mechanism to obtain conversation representations.…”

Section: Hypergraph Neural Networkmentioning

confidence: 99%

Multimodal Emotion Recognition in Conversation Based on Hypergraphs

Li,

Mei,

Jia

et al. 2023

Electronics

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In recent years, sentiment analysis in conversation has garnered increasing attention due to its widespread applications in areas such as social media analytics, sentiment mining, and electronic healthcare. Existing research primarily focuses on sequence learning and graph-based approaches, yet they overlook the high-order interactions between different modalities and the long-term dependencies within each modality. To address these problems, this paper proposes a novel hypergraph-based method for multimodal emotion recognition in conversation (MER-HGraph). MER-HGraph extracts features from three modalities: acoustic, text, and visual. It treats each modality utterance in a conversation as a node and constructs intra-modal hypergraphs (Intra-HGraph) and inter-modal hypergraphs (Inter-HGraph) using hyperedges. The hypergraphs are then updated using hypergraph convolutional networks. Additionally, to mitigate noise in acoustic data and mitigate the impact of fixed time scales, we introduce a dynamic time window module to capture local-global information from acoustic signals. Extensive experiments on the IEMOCAP and MELD datasets demonstrate that MER-HGraph outperforms existing models in multimodal emotion recognition tasks, leveraging high-order information from multimodal data to enhance recognition capabilities.

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Section: Hypergraph Neural Networkmentioning

confidence: 99%