Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation-Reference-Cited by-同舟云学术

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Published:2021-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Web Conference 2021
language:
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Author:

Yu Junliang¹,Yin Hongzhi¹,Li Jundong²,Wang Qinyong¹,Hung Nguyen Quoc Viet³,Zhang Xiangliang⁴

Affiliation:

1. University of Queensland, Australia

2. University of Virginia, USA

3. Griffith University, Australia

4. King Abdullah University of Science and Technology, Saudi Arabia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3442381.3449844

Reference64 articles.

1. Philip Bachman R Devon Hjelm and William Buchwalter. 2019. Learning representations by maximizing mutual information across views. In Advances in Neural Information Processing Systems. 15535–15545. Philip Bachman R Devon Hjelm and William Buchwalter. 2019. Learning representations by maximizing mutual information across views. In Advances in Neural Information Processing Systems. 15535–15545.

2. Song Bai Feihu Zhang and Philip HS Torr. 2019. Hypergraph convolution and hypergraph attention. arXiv preprint arXiv:1901.08150(2019). Song Bai Feihu Zhang and Philip HS Torr. 2019. Hypergraph convolution and hypergraph attention. arXiv preprint arXiv:1901.08150(2019).

3. Hypergraph Theory

4. Music recommendation by unified hypergraph

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