Professor Hongzhi Yin
Professor

Hongzhi Yin

Email: 
Phone: 
+61 7 336 54739

Background

Prof. Hongzhi Yin works as an ARC Future Fellow and Professor and director of the Responsible Big Data Intelligence Lab (RBDI) at The University of Queensland, Australia. He has made notable contributions to recommendation systems, structured foundation model, spatial-temporal prediction, LLM and ChatBI, and decentralized and edge intelligence. He has received numerous awards and recognition for his research achievements. He has been named to IEEE Computer Society’s AI’s 10 to Watch 2022 and Field Leader of Data Mining & Analysis in The Australian's Research 2020 magazine. In addition, he has received the prestigious 2023 Young Tall Poppy Science Awards, Australian Research Council Future Fellowship 2021, the Discovery Early Career Researcher Award 2016, UQ Foundation Research Excellence Award 2019, 2024 and 2025 Computer Science in Australia Leader Award, AI 2000 Most Influential Scholar Honorable Mention in Data Mining (2022-2025), 2024 and 2025 ScholarGPS Highly Ranked Scholar (top 0.05%). His research has won 8 international and national Best Paper Awards, including Best Student Full Paper Award at CIKM 2024, Best Paper Award - Honorable Mention at WSDM 2023, Best Paper Award at ICDE 2019, Best Student Paper Award at DASFAA 2020, Best Paper Award Nomination at ICDM 2018, ACM Computing Reviews' 21 Annual Best of Computing Notable Books and Articles, Best Paper Award at ADC 2018 and 2016. His Ph.D. thesis won Peking University Outstanding Ph.D. Dissertation Award 2014 and CCF Outstanding Ph.D. Dissertation Award (Nomination) 2014. He has ten conference papers recognized as the Most Influential Papers in Paper Digest, including KDD 2021 and 2013, AAAI 2021, SIGIR 2022, WWW 2023 and 2021, CIKM 2021, 2019, 2016, and 2015. He has published 400 papers with an H-index of 91 (29000+ citations), including 290+ CCF A/CORE A* and 90+ CCF B/CORE A, such as ICML, ICLR, NeurIPS, KDD, SIGIR, WWW, ACL, WSDM, SIGMOD, VLDB, ICDE, AAAI, IJCAI, ACM Multimedia, ECCV, IEEE TKDE, TNNL, VLDB Journal, and ACM TOIS. He has been the leading author (first/co-first author or corresponding author) for 300. He has been an SPC/PC member for many top conferences, such as AAAI, IJCAI, KDD, ICML, ICLR, NeurIPS, SIGIR, WWW, WSDM, VLDB, ICDE, ICDM, and CIKM. He has been serving as Associate Editor/Guest Editor/Editorial Board for Neural Networks (JCR Q1, CCF B, 中科院一区), Science China Information Sciences (JCR Q1, CCF A, 中科院一区), Data Science and Engineering (JCR Q1, 中科院一区), Journal of Computer Science and Technology (JCST, CCF B), Journal of Social Computing, ACM Transactions on Information Systems 2022-2023 (JCR Q1, CCF A, CORE A, 中科院一区), ACM Transactions on Intelligent Systems and Technology 2020-2021 (JCR Q1), Information Systems 2020-2021 (CORE A*), and World Wide Web 2020-2021 and 2017-2018 (CORE A, CCF B). Dr. Yin has also been attracting wide media coverage, such as The Australian, SBS Radio Interviews, UQ News, Sohu.com, Faculty News of EAIT, IEEE Computer Society, ACM Computing Reviews.

I am now looking for highly motivated Ph.D. students. The University of Queensland ranks in the top 50 as measured by the Performance Ranking of Scientific Papers for World Universities. The University also ranks 40 in the QS World University Rankings and 41 in the US News Best Global Universities Rankings. The University of Queensland is the best in Australia according to the Australian Financial Review (AFR), which has now ranked UQ in the #1 position for 2 consecutive years. Please find the following three PhD scholarships.

Latest News

  1. [20 May 2026] Our ARC Linkage Project 2025 "AI-Powered Design Co-Pilot for Reimagining Australian Single-Family Homes" has been successfully granted and funded.

  2. [16 May 2026] We have three research papers accepted by the top conference KDD 2026 Research Track (CORE A*, CCF A, Acceptance Rate ~18%).

  3. [12 May 2026] I have been recognised in 2026 Edition of Best Scientists in the field of Computer Science and ranked 42 in Australia on Research.com, a leading academic platform.

  4. [8 May 2026] Our research paper "Efficient Prompt Learning for Traffic Forecasting" has been accepted by VLDB Journal (CORE A*, CCF A)

  5. [3 April 2026] We have 3 research papers accepted by the top conference SIGIR 2026.

    • Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems

    • ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems

    • ProEchoMem: Enhancing Long Video Understanding via Multi-Trace Probe-Echo Memory

  6. [27 March 2026] We have successfully secured the opportunity to host the top-tier conference ICDM 2027 in Brisbane.

  7. [24 Feb 2026] We have 3 research papers on ChatBI accepted by the top conferences ICLR 2026 (CORE A*, CCF A) and ICDE 2026 (CORE A*, CCF A).

  8. [21 Feb 2026] I’m pleased to join the Organizing Committee of the premier conference WSDM 2027 as the Conference Awards Co-Chair.

  9. [18 Feb 2026] I’m pleased to join the Organizing Committee of the data mining flagship conference ADMA 2026 as the PC Co-Chair.

  10. [5 Feb 2026] We are organizing a workshop "LLM-UP: LLM-powered User Profiling for Search and Recommendation" at SIGIR 2026.

  11. [26 January 2026] We have 3 papers accepted by the top conference ICLR 2026 (CORE A*).

  12. [20 January 2026] I was invited to serve as Area Chair in the top conference KDD 2026 (CORE A*, CCF A), IJCAI 2026 (CORE A*, CCF A), ARR-ACL 2026 (CORE A*, CCF A) and ICDM 2026 (CORE A*, CCF B).

  13. [14 January] We have two research papers accepted by the top conference WWW 2026 (CORE A*, CCF A). Congratulations to Xinyi and Hung.

  14. [13 January 2025] We have two research papers recognized as ESI Hot Papers and five research papers recognized as ESI Highly Cited Papers.

  15. [19 December 2025] I was invited to serve as Area Chair in the top conference SIGIR 2026 (CORE A*, CCF A) and senior PC member at the top conference ICMR 2026 (CORE A, CCF B).

  16. [9 December 2025] I have been recognized as 2025 ScholarGPS Highly Ranked Scholar (top 0.05% of all scholars), #3 in Data Mining, #8 in Information Engineering.

  17. [8 November 2025] Our research paper "SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World" was accepted by the top conference AAAI 2026 (CCF A and CORE A*). Congratulations to Jiaqi.

  18. [4 November 2025] We have released the first survey on Reasoning-Aware Recommender Systems in the LLM Era.

  19. [28 October 2025] My ARC Discovery Project 2026 "Advancing Federated Learning for Unified Urban Spatio-Temporal Predictions" has been successfully granted and funded.

  20. [13 October 2025] I was invited to be Area Chair for ACL Rolling Review (ARR).

  21. [1 October 2025] I have been recognised in the Stanford/Elsevier Top 2% Scientists List Career Long (2022-2025) and Single Year (2020-2025).

Availability

Professor Hongzhi Yin is:
Available for supervision

Qualifications

  • Postgraduate Diploma, Peking University
  • Doctor of Philosophy, Peking University

Research interests

  • Structured Foundation Model

  • Spatial-temporal Prediction

  • LLM and ChatBI

  • Recommender System and User Modeling

  • Edge Machine Learning and Applications

  • Time Series and Sequence Mining and Prediction

  • Trustworthy Machine Learning and Applications

Research impacts

Prof. Yin is currently directing the Responsible Big Data Intelligence Lab (RBDI). RBDI Lab aims and strives to develop decentralized, on-device, and trustworthy (e.g., privacy-preserving, robust, explainable and fair) data mining and machine learning techniques with theoretical backbones to better discover actionable patterns and intelligence from large-scale, heterogeneous, networked, dynamic and sparse data. RBDI joins forces with other fields such as urban transportation, healthcare, agriculture, E-commerce and marketing to help solve societal, environmental and economic challenges facing humanity in pursuit of a sustainable future. His research has also attracted media coverage, such as The Australian, SBS, UQ News, Faculty News of EAIT, ACM Computing Reviews, 360 News.

Search Professor Hongzhi Yin’s works on UQ eSpace

422 works between 2011 and 2026
All (422) Book (2) Journal Article (168) Edited Outputs (6) Conference Publication (243) Book Chapter (3)

321 - 340 of 422 works

2019

Conference Publication

Rethinking the item order in session-based recommendation with graph neural networks

Qiu, Ruihong, Li, Jingjing, Huang, Zi and Yin, Hongzhi (2019). Rethinking the item order in session-based recommendation with graph neural networks. CIKM '19 28th ACM International Conference on Information and Knowledge Management, Beijing, China, 3 - 7 November, 2019. New York, New York, USA: ACM Press. doi: 10.1145/3357384.3358010

Rethinking the item order in session-based recommendation with graph neural networks

2019

Conference Publication

Online user representation learning across heterogeneous social networks

Wang, Weiqing, Yin, Hongzhi, Du, Xingzhong, Hua, Wen, Li, Yongjun and Nguyen, Quoc Viet Hung (2019). Online user representation learning across heterogeneous social networks. 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), Paris, France, 21-25 July 2019. New York, NY, United States: Association for Computing Machinery (ACM). doi: 10.1145/3331184.3331258

Online user representation learning across heterogeneous social networks

2019

Conference Publication

Semi-supervised Clustering with Deep Metric Learning

Li, Xiaocui, Yin, Hongzhi, Zhou, Ke, Chen, Hongxu, Sadiq, Shazia and Zhou, Xiaofang (2019). Semi-supervised Clustering with Deep Metric Learning. 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22-15 April 2019. Heidelberg, Germany: Springer . doi: 10.1007/978-3-030-18590-9_50

Semi-supervised Clustering with Deep Metric Learning

2019

Conference Publication

Origin-destination matrix prediction via graph convolution: A new perspective of passenger demand modeling

Wang, Yuandong, Wo, Tianyu, Yin, Hongzhi, Xu, Jie, Chen, Hongxu and Zheng, Kai (2019). Origin-destination matrix prediction via graph convolution: A new perspective of passenger demand modeling. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), Anchorage, AK United States, 4-8 August 2019. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3292500.3330877

Origin-destination matrix prediction via graph convolution: A new perspective of passenger demand modeling

2019

Conference Publication

IWSC 2017 chairs' welcome

Zhang, Rui, Li, Jianxin, Yin, Hongzhi, Reynolds, Mark, Cheema, Muhammad Aamir and Chen, Ling (2019). IWSC 2017 chairs' welcome. International Conference on World Wide Web , Perth, WA, Australia, 3-7 April 2017. Geneva, Switzerland: International World Wide Web Conferences Steering Committee.

IWSC 2017 chairs' welcome

2019

Conference Publication

What can history tell us? Identifying relevant sessions for next-item recommendation

Sun, Ke, Qian, Tieyun, Yin, Hongzhi, Chen, Tong, Chen, Yiqi and Chen, Ling (2019). What can history tell us? Identifying relevant sessions for next-item recommendation. 28th ACM International Conference on Information and Knowledge Management, Beijing, China, 3-7 November 2019. New York, United States: Association for Computing Machinery. doi: 10.1145/3357384.3358050

What can history tell us? Identifying relevant sessions for next-item recommendation

2019

Conference Publication

BLOMA: explain collaborative filtering via Boosted Local rank-One Matrix Approximation

Gao, Chongming, Yuan, Shuai, Zhang, Zhong, Yin, Hongzhi and Shao, Junming (2019). BLOMA: explain collaborative filtering via Boosted Local rank-One Matrix Approximation. 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22-25 April 2019. Philadelphia, PA, United States: Elsevier. doi: 10.1007/978-3-030-18590-9_72

BLOMA: explain collaborative filtering via Boosted Local rank-One Matrix Approximation

2019

Conference Publication

Streaming Session-based Recommendation

Guo, Lei, Chen, Tong, Yin, Hongzhi, Zhou, Alexander, Wang, Qinyong and Hung, Nguyen Quoc Viet (2019). Streaming Session-based Recommendation. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), Anchorage, AK United States, 4-8 August 2019. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3292500.3330839

Streaming Session-based Recommendation

2019

Conference Publication

Exploiting centrality information with graph convolutions for network representation learning

Chen, Hongxu, Yin, Hongzhi, Chen, Tong, Nguyen, Quoc Viet Hung, Peng, Wen-Chih and Li, Xue (2019). Exploiting centrality information with graph convolutions for network representation learning. IEEE 35th International Conference on Data Engineering (ICDE), Macau, China, 8-11 April 2019. Piscataway, NJ United States: IEEE Computer Society. doi: 10.1109/ICDE.2019.00059

Exploiting centrality information with graph convolutions for network representation learning

2019

Conference Publication

Find a reasonable ending for stories: Does logic relation help the story cloze test?

Shang, Mingyue, Fu, Zhenxin, Yin, Hongzhi, Tang, Bo, Zhao, Dongyan and Yan, Rui (2019). Find a reasonable ending for stories: Does logic relation help the story cloze test?. 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, HI, United States, 27 January - 1 February, 2019. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence.

Find a reasonable ending for stories: Does logic relation help the story cloze test?

2018

Journal Article

Personalized video recommendation using rich contents from videos

Du, Xingzhong, Yin, Hongzhi, Chen, Ling, Wang, Yang, Yang, Yi and Zhou, Xiaofang (2018). Personalized video recommendation using rich contents from videos. IEEE Transactions on Knowledge and Data Engineering, 32 (3) 8567986, 1-1. doi: 10.1109/TKDE.2018.2885520

Personalized video recommendation using rich contents from videos

2018

Journal Article

TPM: a temporal personalized model for spatial item recommendation

Wang, Weiqing, Yin, Hongzhi, Du, Xingzhong, Nguyen, Quoc Viet Hung and Zhou, Xiaofang (2018). TPM: a temporal personalized model for spatial item recommendation. ACM Transactions on Intelligent Systems and Technology, 9 (6) a61, 1-25. doi: 10.1145/3230706

TPM: a temporal personalized model for spatial item recommendation

2018

Journal Article

Mobi-SAGE-RS: A sparse additive generative model-based mobile application recommender system

Yin, Hongzhi, Wang, Weiqing, Chen, Liang, Du, Xingzhong, Hung Nguyen, Quoc Viet and Huang, Zi (2018). Mobi-SAGE-RS: A sparse additive generative model-based mobile application recommender system. Knowledge-Based Systems, 157, 68-80. doi: 10.1016/j.knosys.2018.05.028

Mobi-SAGE-RS: A sparse additive generative model-based mobile application recommender system

2018

Journal Article

Preface

Zhou, Xiaofang and Yin, Hongzhi (2018). Preface. Journal of Computer Science and Technology, 33 (4), 621-624. doi: 10.1007/s11390-018-1844-1

Preface

2018

Journal Article

A deep dive into user display names across social networks

Li, Yongjun, Peng, You, Zhang, Zhen, Wu, Mingjie, Xu, Quanqing and Yin, Hongzhi (2018). A deep dive into user display names across social networks. Information Sciences, 447, 186-204. doi: 10.1016/j.ins.2018.02.072

A deep dive into user display names across social networks

2018

Journal Article

Matching user accounts based on user generated content across social networks

Li, Yongjun, Zhang, Zhen, Peng, You, Yin, Hongzhi and Xu, Quanqing (2018). Matching user accounts based on user generated content across social networks. Future Generation Computer Systems, 83, 104-115. doi: 10.1016/j.future.2018.01.041

Matching user accounts based on user generated content across social networks

2018

Journal Article

Layered convolutional dictionary learning for sparse coding itemsets

Mansha, Sameen, Lam, Hoang Thanh, Yin, Hongzhi, Kamiran, Faisal and Ali, Mohsen (2018). Layered convolutional dictionary learning for sparse coding itemsets. World Wide Web, 22 (5), 1-15. doi: 10.1007/s11280-018-0565-2

Layered convolutional dictionary learning for sparse coding itemsets

2018

Journal Article

Matching user accounts across social networks based on username and display name

Li, Yongjun, Peng, You, Zhang, Zhen, Yin, Hongzhi and Xu, Quanqing (2018). Matching user accounts across social networks based on username and display name. World Wide Web, 22 (3), 1-23. doi: 10.1007/s11280-018-0571-4

Matching user accounts across social networks based on username and display name

2018

Journal Article

User identity linkage across social networks via linked heterogeneous network embedding

Wang, Yaqing, Feng, Chunyan, Chen, Ling, Yin, Hongzhi, Guo, Caili and Chu, Yunfei (2018). User identity linkage across social networks via linked heterogeneous network embedding. World Wide Web, 22 (6), 1-22. doi: 10.1007/s11280-018-0572-3

User identity linkage across social networks via linked heterogeneous network embedding

2018

Conference Publication

Publishing graph node strength histogram with edge differential privacy

Qian, Qing, Li, Zhixu, Zhao, Pengpeng, Chen, Wei, Yin, Hongzhi and Zhao, Lei (2018). Publishing graph node strength histogram with edge differential privacy. 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, Gold Coast, QLD, Australia, 21-24 May 2018. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-91458-9_5

Publishing graph node strength histogram with edge differential privacy

Current funding

  • 2026 - 2029
    Advancing Federated Learning for Unified Urban Spatio-Temporal Predictions
    ARC Discovery Projects
    Open grant
  • 2025 - 2028
    Revolutionise Australian Strata Management with Large Language Models
    ARC Linkage Projects
    Open grant
  • 2025 - 2028
    Building an Aussie Information Recommendation System You Can Trust
    ARC Linkage Projects
    Open grant
  • 2024 - 2027
    Privacy-Aware and Personalised Explanation Overlays for Recommender Systems (ARC Discovery Project administered by Griffith University)
    ARC Discovery Projects
    Open grant
  • 2022 - 2026
    Decentralised Collaborative Predictive Analytics on Personal Smart Devices
    ARC Future Fellowships
    Open grant
  • 2021 - 2026
    ARC Training Centre for Information Resilience
    ARC Industrial Transformation Training Centres
    Open grant

Past funding

  • 2022 - 2023
    A Secured Smart Sensing and Industry Analytics Facility for Industry 4.0 (ARC LIEF application led by University of Technology Sydney)
    University of Technology Sydney
    Open grant
  • 2020 - 2021
    Developing a Privacy-Preserving and Energy-Efficient Mobile Recommender System Architecture
    UQ Foundation Research Excellence Awards
    Open grant
  • 2019 - 2024
    Challenging Big Data for Scalable, Robust and Real-time Recommendations
    ARC Discovery Projects
    Open grant
  • 2017 - 2020
    Monitoring Social Events for User Online Behaviour Analytics
    ARC Discovery Projects
    Open grant
  • 2016 - 2018
    Mobile User Modeling for Intelligent Recommendation
    ARC Discovery Early Career Researcher Award
    Open grant

Availability

Professor Hongzhi Yin is:
Available for supervision

Looking for a supervisor? Read our advice on how to choose a supervisor.

Available projects

  • Building an Trustworthy Information Recommendation System

    Build a trustworthy information recommender system by spearheading the design and development of cutting-edge LLM4Rec techniques, misinformation filters, and privacy protection mechanisms.

    This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

Supervision history

Current supervision

  • Doctor Philosophy

    Revolutionise Australian Strata Management with Large Language Models

    Principal Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Chain-of-User-Thought for Personalized Agent in Cyber World

    Principal Advisor

  • Doctor Philosophy

    Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    Principal Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    LLM-enhanced Recommender System

    Principal Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Reliable Multimodal Recommender Systems

    Principal Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    Associate Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Robustness Verification of Neural Network

    Associate Advisor

    Other advisors: Dr Naipeng Dong

  • Doctor Philosophy

    Integrated high-throughput material synthesis and characterisation system

    Associate Advisor

    Other advisors: Associate Professor Jingwei Hou

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    Associate Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Scalable and Generalizable Graph Neural Networks

    Associate Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Sustainable On-Device Recommender Systems

    Associate Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Lightweight Graph Neural Networks for Recommendation

    Associate Advisor

    Other advisors: Associate Professor Rocky Chen

Completed supervision

Enquiries

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