I am an Assistant Professor at the Department of Computer Science and Musketeers Foundation Institute of Data Science, at the University of Hong Kong (HKU) . I am the director of Data Intelligence Lab@HKU, with the focus on Data Mining, Information Retrieval, Spatio-Temporal Data Mining, User Behavior Modeling, Recommender Systems, Graph Representation Learning, and Large Language Models. Prior to that, I received my Ph.D. in Computer Science from the University of Notre Dame in USA.

Prospective Students:

I am looking for self-motivated Ph.D. students/Research (Remote) Interns to work together on various data science topics. 

If you are interested in working with me, please drop an email to chaohuang75@gmail.com. Please check Here for details.

Please include your CV (including your publications, ranking/GPA, anything important) with the email.

HKU Students: Please feel free to contact me if you would like to conduct research in my lab.

👨‍💻✨ Our Research Works are Open-Sourced – Explore Them on Our Lab GitHub Repository  🚀

• [03/2024] - 📣 Please check out our released Graph Foundation Model: 🌟OpenGraph🌟

  • 🔥 OpenGraph Uncovers the Potential of the Graph Foundation Model

  • 🔥 Zero-shot Graph Generalization Distilled from LLMs

• [03/2024] - 📣 Please check out our released Heterogenous Graph Language Models: 🌟[KDD'2024] HiGPT🌟

  • 🔥 HiGPT One Model for Any Heterogeneous Graph

  • 🔥 Cross-domain Zero-shot Heterogeneous Graph Learning

  • 🔥 1-shot Beat 60-shot with Graph In-Context Learning

• [02/2024] - 📣 Please check out our released Spatio-Temporal Large Language Models: 🌟[KDD'2024] UrbanGPT🌟

  • 🔥 UrbanGPT empowers LLMs to comprehend the intricate inter-dependencies across time and space

  • 🔥 We facilitate more comprehensive and accurate Spatio-Temporal Predictions under Data Scarcity

• [02/2024] - 📣 Please check out our released Large Language Models for Graph Structure Learning: 🌟GraphEdit🌟

  • 🔥 GraphEdit is a large language model to Effectively Denoises Noisy Connections

  • 🔥 The proposed new framework identifies node-wise dependencies from a global perspective

• [11/2023] - 📣 Please check out our released Graph Large Language Model: 🌟[SIGIR'2024] GraphGPT🌟

  • 🔥 GraphGPT framework aligns LLMs with Graph Structural Knowledge for graph learning

  • 🔥 We integrate Text-Graph Grounding with Instruction Tuning to build Effective and Efficient LLM for Graphs

• [11/2023] - 📣 Please check out our released LLM-enhanced Recommender System: 🌟[WSDM'2024] LLMRec🌟

  • 🔥 LLMRec: Simple yet Effective LLM-based Graph Augmentation strategies for recommendation

  • 🔥 We enhance the understanding of user preference with the Incorporation of LLM-based Knowledge

• [11/2023] - 📣 Please check out our released Representation Learning Framework with LLMs: 🌟[WWW'2024] RLMRec🌟 

  • 🔥 RLMRec: Enhancing existing recommenders with Model-agnostic LLM-empowered Representation Learning

  • 🔥 We integrate Representation Learning with LLMs to capture Intricate Semantic Aspects of User Behaviors

Recent News Highlights  

• [04/2024] - 📣 Please check out our Survey on Self-Supervised Learning for Recommendation

  • A Comprehensive Survey of Self-Supervised Learning (SSL) for Recommendation: Survey Paper

  • A Collection of Papers and Resources about SSL for Recommendation (Rec): Github Repo

• [03/2024] - 📣 Two papers are accepted by SIGIR'2024, Congratulations to Jiabin, Yuxi and Other Collaborators! 🎉🎉

  • Graph Fundation Model with LLMs (GraphGPT)

  • Self-Supervised Graph Neural Networks (SelfGNN)

• [02/2024] - 📣 Three papers are accepted by WWW'2024, Congratulations to Xubin, Yuhao, Wei and Other Collaborators! 🎉🎉

  • Recommendation with LLM-empowered Representation Learning (RLMRec)

  • Unifying Graph Pre-training with Prompt Learning (GraphPro)

  • Multi-Modal Prompt-Tuning for Recommendation (PromptMM)

• [02/2024] - 📣 One paper is accepted by ICDE'2024, Congratulations to Qianru and Other Collaborators! 🎉🎉

  • Graph Augmentation for Recommendation (GraphAug)

• [12/2023] - 📣 Excited to share our latest video highlighting  our recent research investigations of 📺 Graph + LLMs! 📚🔬

  • 🔥 i) Topic 1: Towards Large Model for Graphs; ii) Topic 2: Large Languages Models for Sturctured Data in BioChem

  • 🔥 iii) Topic 3: Graph Instruction Tuning for Large Language Models; iv) Topic 4: Enhancing Recommender Systems with Large Language Models

• [10/2023] - 📣 Please check out our SSLRec: A Self-Supervised Learning Framework for Recommendation

  • 🔥 SSLRec covers various recommendation scenarios: i) General Collaborative Filtering; ii) Sequential Recommendation; 

      iii) Multi-Behavior Recommendation; iv) Social Recommendation; v) Knowledge Graph-enhanced Recommendation

• • 🔥 SSLRec offers a vast array of utility functions and an easy-to-use interface that simplifies the development and evaluation of recommendation models.

• [10/2023] - 📣 Three papers are accepted by WSDM'2024, Congratulations to Wei, Xubin, Yangqin and Other Collaborators! 🎉🎉

  • Simple yet Effective LLM-enhanced Recommender Systems (LLMRec)

  • Self-Supervised Learning Framework for Recommendation (SSLRec)

  • Knowledge Graph Diffusion Model (DiffKG)

• [09/2023] - 📣 Two papers are accepted by NeurIPS'2023, Congratulations to Zhonghang, Xu and Other Collaborators! 🎉🎉

  • Generative Pre-Training of Spatio-Temporal Graph Neural Networks (GPT-ST)

  • LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting (LargeST)

• [08/2023] - 📣 Three papers are accepted by CIKM'2023, Congratulations to Xuheng, Jiabin and Other Collaborators! 🎉🎉

  • Explainable Spatio-Temporal Graph Neural Networks (STExplainer)

  • Spatio-Temporal Meta Contrastive Learning (CL4ST)

  • Expressive Graph Neural Networks for Recommendation (GTE)

• [07/2023] - 📣 Our paper is accepted by Recsys'2023, Congratulations to Wei and Other Collaborators! 🎉🎉

  • Multi-Relational Contrastive Learning (RCL)

• [05/2023] - 📣 Our Works (HCCF and KGCL) are featured in 🌟 Most Influential Papers of SIGIR 2022 (Rank 2nd & 3rd), Congratulations to All Authors! 🎉🎉

• [05/2023] - 📣 Two papers are accepted by KDD'2023, Congratulations to Yuhao, Yangqin and Other Collaborators! 🎉🎉

  • Knowledge Graph Self-Supervised Rationalization (KGRec)

  • Adaptive Graph Contrastive Learning (AdaGCL)

• [05/2023] - 📣 Our Work (AutoCF) is featured as 🌟 Spotlight Paper of WWW'2023 (16/323), Congratulations to All Authors! 🎉🎉

• [04/2023] - 📣 Our paper is accepted by ICML'2023, Congratulations to Qianru and Other Collaborators! 🎉🎉

  • Spatio-Temporal Graph Pre-training (GraphST)

• [04/2023] - 📣 Three papers are accepted by SIGIR'2023, Congratulations to Xubin, Yaowen, Chaoliu and Other Collaborators! 

  • Disentangled Contrastive Collaborative Filtering (DCCF);  Graph Transformer (GFormer);  Graph Masked Autoencoder (MAERec)

• [04/2023] - 📣 One paper is accepted by IJCAI'2023, Congratulations to Tianle and Other Collaborators! 🎉🎉

  • Denoised Self-Augmented Learning (DSL)

• [01/2023] - 📣 Five papers are accepted by WWW'2023, Congratulations to Lianghao, Wei, Yuhao, Qianru and Other Collaborators! 🎉🎉

  • Automated Self-Supervised Learning (AutoCF);  Graph-less Collaborative Filtering (SimRec);  Multi-Modal Self-Supervised Learning (MMSSL)

  • Automated Spatio-Temporal Graph Contrastive Learning (AutoST);  Debiased Contrastive Learning (DCRec)

• [01/2023] - 📣 One paper is accepted by ICLR'2023, Congratulations to Xuheng and Other Collaborators! 🎉🎉

  • Simple yet Effective Graph Contrastive Learning  (LightGCL)

• [01/2023] - 📣 One paper is accepted by ICDE'2023, Congratulations to Lianghao and Other Collaborators! 🎉🎉

  • Disentangled Graph Neural Networks  (DGNN)

• [10/2022] - 📣 One paper is accepted by WSDM'2023, Congratulations to Mengru and Other Collaborators! 🎉🎉

  • Heterogenous Graph Contrastive Learning  (HGCL)

• [05/2022] - 📣 Three papers are accepted by KDD'2022, Congratulations to Lianghao, Yuhao and Other Collaborators! 🎉🎉

  • Self-Supervised Hypergraph Transformer (SHT);  Multi-Behavior Sequential Recommendation (MBHT); 

  • Multiplex Heterogeneous Graph Convolutional Network  (MHGCN)

• [04/2022] - 📣 Two papers are accepted by SIGIR'2022, Congratulations to Lianghao, Yuhao and Other Collaborators! 🎉🎉

  • Hypergraph Contrastive Collaborative Filtering (HCCF);  Knowledge Graph Contrastive Learning (KGCL)

• [11/2021] - 📣 Our Research Work CML has been selected as 🌟 Best Paper Candidate of WSDM'2022, Congratulations to All Authors!

• [10/2021] - 📣 One paper is accepted by WSDM'2022, Congratulations to Wei and Other Collaborators! 🎉🎉

  • Contrastive Meta Learning  (CML)

Research Highlights:

(In chronological order, * indicates corresponding author, + indicates supervised student)

• [SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"

J. Tang+, Y. Yang, W. Wei, L. Shi, L. Su, S. Cheng, D. Yin and C. Huang*

🌟 (Top-2 Most Cited Paper: 2 / 159 Accepted Papers) 🌟

[pdf] [code]

• [WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"

X. Ren+, W. Wei, L. Xia, L. Su, S. Cheng, J. Wang, D. Yin and C. Huang*

🌟 (Top-1 Most Cited Paper: 1 / 405 Accepted Papers) 🌟

[pdf] [code]

• [WSDM'2024] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"

W. Wei+, X. Ren, J. Tang, Q. Wang, L. Su, S. Cheng, J. Wang, D. Yin and C. Huang*

🌟 (Top-1 Most Cited Paper: 1 / 112 Accepted Papers) 🌟

[pdf] [code]

• [WWW'2023] "Automated Self-Supervised Learning for Recommendation"

L. Xia+, C. Huang*, C. Huang, K. Lin, T. Yu and B. Kao

🌟 (Spotlight Paper & Best Paper Nomination: 16/323 Accepted Papers) 🌟

[pdf] [code]

• [WWW'2023] "Multi-Modal Self-Supervised Learning for Recommendation"

W. Wei+, C. Huang*, L. Xia and C. Zhang

🌟 (Most Influential Papers of WWW'2023 - Rank 2nd /323 Accepted Papers) 🌟

[pdf] [code]

• [WWW'2023] "Debiased Contrastive Learning for Sequential Recommendation"

Y. Yang+, C. Huang*, L. Xia, C. Huang, D. Luo and K. Li

🌟 (Most Influential Papers of WWW'2023 - Rank 4th /323 Accepted Papers) 🌟

[pdf] [code]

• [SIGIR'2023] "Disentangled Contrastive Collaborative Filtering"

X. Ren+, L. Xia, J. Zhao, D. Yin and C. Huang*

🌟 (Most Influential Papers of SIGIR'2023 - Rank 12th / 165 Accepted Papers) 🌟

[pdf] [code]

• [ICLR'2023] "LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation"

X. Cai+, C. Huang*, L. Xia and X. Ren

🌟 (Selected as Spotlight Paper: 25%) 🌟

[pdf] [code]

• [WSDM'2023] "Heterogeneous Graph Contrastive Learning for Recommendation"

M. Chen+, C. Huang*, L. Xia,  W. Wei, Y. Xu and R. Luo

🌟 (Top-2 Most Cited Paper: 2 / 123 Accepted Papers) 🌟

[pdf] [code]

• [SIGIR'2022] "Hypergraph Contrastive Collaborative Filtering"

L. Xia+, C. Huang*, Y. Xu, J. Zhao, D. Yin and J. Huang

ACM Special Interest Group on Information Retrieval

🌟 (Most Influential Papers of SIGIR'2022 - Rank 2nd / 161 Accepted Papers) 🌟

[pdf] [code]

• [SIGIR'2022] "Knowledge Graph Contrastive Learning for Recommendation"

Y. Yang+, C. Huang*, L. Xia and C. Li

ACM Special Interest Group on Information Retrieval

🌟 (Most Influential Papers of SIGIR'2022 - Rank 3rd / 161 Accepted Papers) 🌟

[pdf] [code]

• [WSDM'2022] "Contrastive Meta Learning with Behavior Multiplicity for Recommendation"

W. Wei+, C. Huang*, L. Xia,  Y. Xu, J. Zhao and D. Yin

ACM International Conference on Web Search and Data Mining

🌟 (Best Paper Nomination & Top-3 Most Cited Paper 3 / 159 Accepted Papers) 🌟

[pdf] [code]

• [SIGIR'2021] "Graph Meta Network for Multi-Behavior Recommendation" 

L. Xia+, Y. Xu, C. Huang*, P. Dai and L. Bo

🌟 (Most Influential Papers of SIGIR'2021 - Rank 14th / 151 Accepted Papers) 🌟

[pdf] [code]

• [WWW'2019] "Mist: A Multiview and Multimodal Spatial-Temporal Learning Framework for Citywide Abnormal Event Forecasting" 

C. Huang*, C. Zhang, J. Zhao, X. Wu, D. Yin and N. Chawla

🌟 (Best Paper Nomination) 🌟

[pdf] [code]

• [KDD'2019] "Heterogeneous Graph Neural Network" 

C. Zhang, D. Song, C. Huang, A. Swami, N. Chawla

🌟 (Most Influential Papers of KDD'2019 - Rank 3rd / 174 Accepted Papers) 🌟

[pdf] [code]