Towards Lightweight Cross-Domain Sequential Recommendation via External Attention-Enhanced Graph Convolution Network

Zhang, Jinyu; Duan, Huichuan; Guo, Lei; Xu, Linhao; Wang, Xinhua

doi:10.1007/978-3-031-30672-3_14

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Cited by 4 publications

References 31 publications

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Contrastive cross-domain sequential recommendation via emphasized intention features

Ni,

Cai,

Jiang

2024

Neural Networks

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Contrastive cross-domain sequential recommendation via emphasized intention features

Ni,

Cai,

Jiang

2024

Neural Networks

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Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation

Wang,

Yue,

Wang

et al. 2023

2023 IEEE International Conference on Data Mining Workshops (ICDMW)

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Contrastive Multi-view Interest Learning for Cross-domain Sequential Recommendation

Zang,

Zhu,

Zhang

et al. 2023

ACM Trans. Inf. Syst.

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Cross-domain recommendation (CDR), which leverages information collected from other domains, has been empirically demonstrated to effectively alleviate data sparsity and cold-start problems encountered in traditional recommendation systems. However, current CDR methods, including those considering time information, do not jointly model the general and current interests within and across domains, which is pivotal for accurately predicting users’ future interactions. In this paper, we propose a Contrastive learning enhanced Multi-View interest learning model (CMVCDR) for cross-domain sequential recommendation. Specifically, we design a static view and a sequential view to model uses’ general interests and current interests, respectively. We divide a user’s general interest representation into a domain-invariant part and a domain-specific part. A cross-domain contrastive learning objective is introduced to impose constraints for optimizing these representations. In the sequential view, we first devise an attention mechanism guided by users’ domain-invariant interest representations to distill cross-domain knowledge pertaining to domain-invariant factors while reducing noise from irrelevant factors. We further design a domain-specific interest-guided temporal information aggregation mechanism to generate users’ current interest representations. Extensive experiments demonstrate the effectiveness of our proposed model compared with state-of-the-art methods.

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Towards Lightweight Cross-Domain Sequential Recommendation via External Attention-Enhanced Graph Convolution Network

Cited by 4 publications

References 31 publications

Contrastive cross-domain sequential recommendation via emphasized intention features

Contrastive cross-domain sequential recommendation via emphasized intention features

Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation

Contrastive Multi-view Interest Learning for Cross-domain Sequential Recommendation

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