DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation

Guo, Lei; Tang, Li; Chen, Tong; Zhu, Lei; Nguyen, Quoc Viet Hung; Yin, Hongzhi

doi:10.24963/ijcai.2021/342

DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation

Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, Hongzhi Yin

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 2483-2489.

https://doi.org/10.24963/ijcai.2021/342

Keywords

Humans and AI: Personalization and User Modeling
Machine Learning: Recommender Systems
Data Mining: Information Retrieval

Abstract

Shared-account Cross-domain Sequential Recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains. Existing work on solving SCSR mainly relies on mining sequential patterns via RNN-based models, which are not expressive enough to capture the relationships among multiple entities. Moreover, all existing algorithms try to bridge two domains via knowledge transfer in the latent space, and the explicit cross-domain graph structure is unexploited. In this work, we propose a novel graph-based solution, namely DA-GCN, to address the above challenges. Specifically, we first link users and items in each domain as a graph. Then, we devise a domain-aware graph convolution network to learn user-specific node representations. To fully account for users' domain-specific preferences on items, two novel attention mechanisms are further developed to selectively guide the message passing process. Extensive experiments on two real-world datasets are conducted to demonstrate the superiority of our DA-GCN method.

Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2021. DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 2483–2489. https://doi.org/10.24963/ijcai.2021/342

@inproceedings{ijcai2021p342,
  title     = {DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation},
  author    = {Guo, Lei and Tang, Li and Chen, Tong and Zhu, Lei and Nguyen, Quoc Viet Hung and Yin, Hongzhi},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {2483--2489},
  year      = {2021},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2021/342},
  url       = {https://doi.org/10.24963/ijcai.2021/342},
}

TY  - CPAPER
TI  - DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation
AU  - Guo, Lei
AU  - Tang, Li
AU  - Chen, Tong
AU  - Zhu, Lei
AU  - Nguyen, Quoc Viet Hung
AU  - Yin, Hongzhi
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 2483
EP  - 2489
DO  - 10.24963/ijcai.2021/342
UR  - https://doi.org/10.24963/ijcai.2021/342
ER  -