Unleashing the Power of Knowledge Graph for Recommendation via Invariant Learning-Reference-Cited by-同舟云学术

Unleashing the Power of Knowledge Graph for Recommendation via Invariant Learning

Published:2024-05-13 Issue: Volume:34 Page:3745-3755
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Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Wang Shuyao¹^ORCID,Sui Yongduo¹^ORCID,Wang Chao²^ORCID,Xiong Hui³^ORCID

Affiliation:

1. School of Data Science, University of Science and Technology of China, Hefei, China

2. Guangzhou HKUST Fok Ying Tung Research Institute & The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

3. The Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou) & The Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Guangzhou, China

Funder

Project funded by China Postdoctoral Science Foundation

National Natural Science Foundation of China

Science and Technology Planning Project of Guangdong Province

the Foshan HKUST Projects

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645576

Reference77 articles.

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2. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

3. Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019).

4. Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017).

5. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. NeurIPS , Vol. 26 (2013).