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HKU-IDS Scholar

Dr Chao HUANG
Assistant Professor
HKU Musketeers Foundation Institute of Data Science and
Department of Computer Science, HKU
chuang@cs.hku.hk (852) 2559 8447
CB-318, Composite Building, HKU
Department of Computer Science
Key expertise

Machine Learning, Deep Learning, Data Mining 

About me

Dr Chao Huang is a tenure-track assistant professor at the University of Hong Kong. He is a faculty member of the Institute of Data Science and Department of Computer Science. Before that, he was a research scientist at JD Research America in Silicon Valley. He obtained the Ph.D degree from the Computing Science and Engineering Department at University of Notre Dame in United States.

Current Research Project

Dr Huang, through his research, aims to achieve a long-term goal of developing effective, expressive, efficient and interpretable machine learning solutions, to distill useful knowledge from the complex data and facilitate various underlying applications with across different disciplines. Applications include, web services, recommender systems, smart cities, and computational social science.

Selected Publications
  • Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, and Jimmy Xiangji Huang. “Hypergraph Contrastive Collaborative Filtering.” ACM International Conference on Research and Development in Information Retrieval (SIGIR), DOI: https://doi.org/10.48550/arXiv.2204.12200, (2022).
  • Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. “Knowledge Graph Contrastive Learning for Recommendation.” ACM International Conference on Research and Development in Information Retrieval (SIGIR), DOI: https://doi.org/10.48550/arXiv.2205.00976, (2022).
  • Wei Wei, Chao Huang, Lianghao Xia, Yong Xu, Jiashu Zhao, and Dawei Yin. “Meta Learning with Behavior Multiplicity for Recommendation.” ACM International Conference on Web Search and Data Mining (WSDM), DOI: https://doi.org/10.1145/3488560.3498527, (2022).
  • Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, and Yanfang Ye. “Knowledge-aware coupled graph neural network for social recommendation.” AAAI Conference on Artificial Intelligence (AAAI), 35(5), 4115-4122, DOI: https://doi.org/10.1609/aaai.v35i5.16533, (2021).
  • Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, and Liefeng Bo. “Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation.” AAAI Conference on Artificial Intelligence (AAAI), 35(5), 4486-4493, DOI: https://doi.org/10.1609/aaai.v35i5.16576, (2021).
  • Xiyue Zhang, Chao Huang, Yong Xu, Lianghao Xia, Peng Dai, Liefeng Bo, Junbo Zhang, and Yu Zheng. “Traffic flow forecasting with spatial-temporal graph diffusion network.” AAAI Conference on Artificial Intelligence (AAAI), DOI: https://doi.org/10.48550/arXiv.2110.04038, (2021).
  • Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, and Liefeng Bo. “Graph meta network for multi-behavior recommendation.” ACM International Conference on Research and Development in Information Retrieval (SIGIR), DOI: https://doi.org/10.48550/arXiv.2110.03969, (2021).
  • Chao Huang, Jiahui Chen, Lianghao Xia, Yong Xu, Peng Dai, Yanqing Chen, Liefeng Bo, Jiashu Zhao, and Jimmy Xiangji Huang. “Graph-enhanced multi-task learning of multi-level transition dynamics for session-based recommendation.” AAAI Conference on Artificial Intelligence (AAAI), 35(5), 4123-4130, DOI: https://doi.org/10.1609/aaai.v35i5.16534, (2021).
  • Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Bo Zhang, and Liefeng Bo. “Multiplex behavioral relation learning for recommendation via memory augmented transformer network.” ACM International Conference on Research and Development in Information Retrieval (SIGIR), DOI: https://doi.org/10.48550/arXiv.2110.04002, (2020).
  • Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V Chawla. “Few-shot knowledge graph completion.” AAAI Conference on Artificial Intelligence (AAAI), DOI: https://doi.org/10.48550/arXiv.1911.11298, (2020).

Research Interests

Data Mining, Machine Learning, Deep Neural Networks

Awards
Invited Presentations
  • Talks on “Knowledge Graph Contrastive Learning for Recommendation” (with Yuhao Yang, Lianghao Xia and Chenliang Li) & “Hypergraph Contrastive Collaborative Filtering” (with Lianghao Xia, Yong Xu, Jiashu Zhao, Dawei Yin and Jimmy Huang). The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), July 2022.
  • Talk at The 31st ACM International Conference on Information and Knowledge Management (CIKM), October 2022.

 

RPg Students
Mr. Yuhao Yang (PhD)
Ms. Wei Wei (PhD)