DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation
Author:
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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