DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation-Reference-Cited by-同舟云学术

DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation

Published:2020-10-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Information & Knowledge Management
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Author:

Wang Yifan¹,Tang Suyao¹,Lei Yuntong¹,Song Weiping¹,Wang Sheng²,Zhang Ming¹

Affiliation:

1. Peking University, Beijing, China

2. University of Washington, Seattle, WA, USA

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Meituan-Dianping Group

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3340531.3411996

Reference43 articles.

1. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

2. Representation Learning: A Review and New Perspectives

3. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences

4. Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever and Pieter Abbeel. 2016. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. In Advances in Neural Information Processing Systems. 2172--2180. Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever and Pieter Abbeel. 2016. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. In Advances in Neural Information Processing Systems. 2172--2180.

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