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 predictive analytics, recommendation systems, graph learning, social media analytics, 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, Rising Star of Science Award (2023 and 2022) and 2024 Computer Science in Australia Leader Award, 2024 Computer Science in Australia Leader Award, AI 2000 Most Influential Scholar Honorable Mention in Data Mining (2024, 2023 and 2022). His research has won 8 international and national Best Paper Awards, including 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 over 300 papers with an H-index of 75, including 200+ CCF A and 80+ CCF B, 200+ CORE A* and 80+ CORE A, such as KDD, SIGIR, WWW, 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 200+. 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, Chinese Academy of Sciences ranking Q1, and CCF B), Science China Information Sciences (JCR Q1, Chinese Academy of Sciences ranking Q1, and CCF A), Data Science and Engineering (DSE, JCR Q1, Chinese Academy of Sciences ranking Q2), Journal of Computer Science and Technology (JCST, CCF B), Journal of Social Computing, ACM Transactions on Information Systems 2022-2023 (TOIS, CCF A), ACM Transactions on Intelligent Systems and Technology 2020-2021 (TIST, 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.

Dr. Hongzhi Yin is looking for highly motivated and high-quality 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 47 in the QS World University Rankings, 52 in the US News Best Global Universities Rankings, 60 in the Times Higher Education World University Rankings, and 55 in the Academic Ranking of World Universities.

Latest News

  1. [2 July 2024] I have been invited to serve as area chair at KDD 2025.

  2. [27 June 2024] Our ARC Linkage Project "Building an Aussie Information Recommendation System You Can Trust" has been granted and funded.

  3. [16 June 2024] I have been invited to co-chair the User modeling, personalization and recommendation track at The Web Conference 2025.

  4. [6 June 2024] Recently, we have released 2 comprehensive survey papers.

  5. [23 May 2024] Our project Personalized On-Device Large Language Models was shortlisted as a finalist for the 2024 iAwards.

  6. [22 May 2024] Our research paper "Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion" has been accepted by the top journal TOIS 2024 (CORE A and CCF A).

  7. [17 May 2024] We have 4 full research research papers accepted by the prestigious conference KDD 2024 (CORE A*, CCF A).

  8. [24 April 2024] I have been recognized with 2024 Computer Science in Australia Leader Award in Research.com.

  9. [26 March 2024] We have three research papers accepted by the top conference SIGIR 2024 (CORE A*, CCF A).

  10. [14 March 2024] I have again been recognized as the 2024 AI 2000 Most Influential Scholar Honorable Mention in Data Mining.

  11. [10 March 2024] We have 8 research papers accepted by the prestigious conference ICDE 2024 (CORE A*, CCF A), including 4 accepted in the first round and 4 in the second round.

  12. [13 February 2024] Congratulations to Dr. Junliang Yu, my Ph.D. graduate, on winning the UQ Graduate School 2023 Dean's Award for Outstanding Higher Degree by Research Theses.

  13. [11 February 2024] We have 2 research papers directly accepted in the second round of the prestigious conference ICDE 2024 (CORE A*, CCF A). It's noteworthy that out of over 1000 submissions, only 19 were directly accepted.

  14. [2 February 2024] We are organizing a special issue, "Cloud-Edge Collaboration for On-Device Recommendation", in the top journal - Science China Information Sciences (CCF Ranking A, CIC Ranking A, CAA Ranking A ), and call for paper is online.

  15. [31 January 2024] Our research paper "Personalized Elastic Embedding Learning for On-Device Recommendation" has been accepted by the top journal TKDE 2024 (CORE A* and CCF A).

  16. [24 January 2024] We have five research papers and one tutorial accepted by The Web Conference 2024 (CORE A*, CCF A).

  17. [23 January 2024] We have released three timely surveys:

  18. [19 January 2024] I have been invited to serve as Official Nominator for VinFuture Prize (US$3,000,000). The nomination is open!

  19. [13 January 2024] I have been invited to serve as Area Chair in the Research Track of KDD 2024.

  20. [1 January 2024] I began to serve as Action/Associate Editor for Neural Networks (JCR Q1, Chinese Academy of Sciences ranking Q1, and CCF B), Data Science and Engineering (DSE, JCR Q1, Chinese Academy of Sciences ranking Q2).

  21. [1 January 2024] I have been promoted to Professor (Level E) at The University of Queensland.

Availability

Professor Hongzhi Yin is:
Available for supervision

Qualifications

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

Research interests

  • Recommender System and User Modeling

  • Graph Mining and Embedding

  • Decentralized and Federated Learning

  • Edge Machine Learning and Applications

  • Trustworthy Machine Learning and Applications

  • QA, Chatbot and Information Retrieval

  • Time Series and Sequence Mining and Prediction

  • Spatiotemporal Data Mining

  • Smart Healthcare

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

338 works between 2011 and 2024
All (338) Journal Article (128) Book Chapter (2) Conference Publication (200) Book (2) Edited Outputs (6)

321 - 338 of 338 works

2015

Journal Article

Modeling location-based user rating profiles for personalized recommendation

Yin, Hongzhi, Cui, Bin, Chen, Ling, Hu, Zhiting and Zhang, Chengqi (2015). Modeling location-based user rating profiles for personalized recommendation. ACM Transactions on Knowledge Discovery from Data, 9 (3) 19, 19:1-19:41. doi: 10.1145/2663356

Modeling location-based user rating profiles for personalized recommendation

2015

Journal Article

Dynamic user modeling in social media systems

Yin, Hongzhi, Cui, Bin, Chen, Ling, Hu, Zhiting and Zhou, Xiaofang (2015). Dynamic user modeling in social media systems. ACM Transactions on Information Systems, 33 (3) 10, 10:1-10:44. doi: 10.1145/2699670

Dynamic user modeling in social media systems

2015

Conference Publication

Geographical constraint and temporal similarity modeling for point-of-interest recommendation

Wu, Huimin, Shao, Jie, Yin, Hongzhi, Shen, Heng Tao and Zhou, Xiaofang (2015). Geographical constraint and temporal similarity modeling for point-of-interest recommendation. International Conference on Web Information Systems Engineering, Miami, FL, United States, 1-3 November 2015. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-26187-4_40

Geographical constraint and temporal similarity modeling for point-of-interest recommendation

2015

Conference Publication

Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion

Xie, Yiran, Yin, Hongzhi, Cui, Bin, Yao, Junjie and Xu, Quanqing (2015). Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion. 31st IEEE International Conference on Data Engineering Workshops 2015, Seoul, South Korea, 13-17 April 2015. IEEE Computer Society. doi: 10.1109/ICDEW.2015.7129573

Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion

2015

Conference Publication

Discovering Organized POI Groups in a city

Xu, Yanxia, Liu, Guanfeng, Yin, Hongzhi, Xu, Jiajie, Zheng, Kai and Zhao, Lei (2015). Discovering Organized POI Groups in a city. 20th International Conference on Database Systems for Advanced Applications (DASFAA), Hanoi, Vietnam, 20-23 April 2015. Heidelberg, Germany: Springer International Publishing. doi: 10.1007/978-3-319-22324-7_19

Discovering Organized POI Groups in a city

2015

Journal Article

Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion

Xie, Yiran, Yin, Hongzhi, Cui, Bin, Yao, Junjie and Xu, Quanqing (2015). Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion. World Wide Web, 19 (6), 1-28. doi: 10.1007/s11280-015-0379-4

Distinguishing re-sharing behaviors from re-creating behaviors in information diffusion

2015

Conference Publication

Joint modeling of users' interests and mobility patterns for point-of-interest recommendation

Yin, Hongzhi, Cui, Bin, Huang, Zi, Wang, Weiqing, Wu, Xian and Zhou, Xiaofang (2015). Joint modeling of users' interests and mobility patterns for point-of-interest recommendation. 23rd ACM International Conference on Multimedia, MM 2015, Brisbane, QLD, Australia, 26-30 October, 2015. New York, NY, United States: Association for Computing Machinery, Inc. doi: 10.1145/2733373.2806339

Joint modeling of users' interests and mobility patterns for point-of-interest recommendation

2015

Conference Publication

Predicting passengers in public transportation using smart card data

Dou, Mengyu, He, Tieke, Yin, Hongzhi, Zhou, Xiaofang, Chen, Zhenyu and Luo, Bin (2015). Predicting passengers in public transportation using smart card data. 26th Australasian Database Conference (ADC), Melbourne Australia, 4-7 June 2015. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-19548-3_3

Predicting passengers in public transportation using smart card data

2015

Conference Publication

Geo-SAGE: a geographical sparse additive generative model for spatial item recommendation

Wang, Weiqing, Yin, Hongzhi, Chen, Ling, Sun, Yizhou, Sadiq, Shazia and Zhou, Xiaofang (2015). Geo-SAGE: a geographical sparse additive generative model for spatial item recommendation. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 10-13 August 2015. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/2783258.2783335

Geo-SAGE: a geographical sparse additive generative model for spatial item recommendation

2015

Conference Publication

Joint modeling of user check-in behaviors for point-of-interest recommendation

Yin, Hongzhi, Zhou, Xiaofang, Shao, Yingxia, Wang, Hao and Sadiq, Shazia (2015). Joint modeling of user check-in behaviors for point-of-interest recommendation. 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourme, VIC, Australia, 19-23 October, 2015. New York , NY, United States: Association for Computing Machinery. doi: 10.1145/2806416.2806500

Joint modeling of user check-in behaviors for point-of-interest recommendation

2015

Conference Publication

Predicting users' purchasing behaviors using their browsing history

He, Tieke, Yin, Hongzhi, Chen, Zhenyu, Zhou, Xiaofang and Luo, Bin (2015). Predicting users' purchasing behaviors using their browsing history. 26th Australasian Database Conference (ADC), Melbourne, Australia, 4-7 June 2015. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-19548-3_11

Predicting users' purchasing behaviors using their browsing history

2014

Journal Article

LCARS: A spatial item recommender system

Yin, Hongzhi, Cui, Bin, Sun, Yizhou, Hu, Zhiting and Chen, Ling (2014). LCARS: A spatial item recommender system. ACM Transactions on Information Systems, 32 (3) 11, 11-11. doi: 10.1145/2629461

LCARS: A spatial item recommender system

2014

Conference Publication

A temporal context-aware model for user behavior modeling in social media systems

Yin, Hongzhi, Cui, Bin, Chen, Ling, Hu, Zhiting and Huang, Zi (2014). A temporal context-aware model for user behavior modeling in social media systems. 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014, Snowbird, UT United States, 22-27 June 2014. New York, NY United States: Association for Computing Machinery. doi: 10.1145/2588555.2593685

A temporal context-aware model for user behavior modeling in social media systems

2013

Conference Publication

LCARS: a location-content-aware recommender system

Yin, Hongzhi, Sun, Yizhou, Cui, Bin, Hu, Zhiting and Chen, Ling (2013). LCARS: a location-content-aware recommender system. 19th ACM SIGKDD Knowledge Discovery and Data Mining, Chicago, IL, United States, 11-14 August 2013. New York, NY, United States: ACM. doi: 10.1145/2487575.2487608

LCARS: a location-content-aware recommender system

2013

Conference Publication

A unified model for stable and temporal topic detection from social media data

Yin, Hongzhi, Cui, Bin, Lu, Hua, Huang, Yuxin and Yao, Junjie (2013). A unified model for stable and temporal topic detection from social media data. International Conference on Data Engineering, Brisbane, Australia, 8-11 April 2013. Washington, DC, United States: I E E E Computer Society. doi: 10.1109/ICDE.2013.6544864

A unified model for stable and temporal topic detection from social media data

2013

Conference Publication

TeRec: a temporal recommender system over tweet stream

Chen, Chen, Yin, Hongzhi, Yao, Junjie and Cui, Bin (2013). TeRec: a temporal recommender system over tweet stream. VLDB2013: 39th International Conference on Very Large Data Bases, Riva del Garda, Trento, Italy, 26-30 August, 2013. New York, NY, USA: Association for Computing Machinery. doi: 10.14778/2536274.2536289

TeRec: a temporal recommender system over tweet stream

2012

Conference Publication

Challenging the long tail recommendation

Yin, Hongzhi, Cui, Bin, Li, Jing, Yao, Junjie and Chen, Chen (2012). Challenging the long tail recommendation. 38th International Conference on Very Large Data Bases 2012, (VLDB 2012), Istanbul, Turkey, 27-31 August 2012. New York, NY United States: Association for Computing Machinery. doi: 10.14778/2311906.2311916

Challenging the long tail recommendation

2011

Conference Publication

Finding a wise group of experts in social networks

Yin, Hongzhi, Cui, Bin and Huang, Yuxin (2011). Finding a wise group of experts in social networks. ADMA 2011: 7th International Conference on Advanced Data Mining and Applications, Beijing, China, 17-19 December, 2011. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-25853-4_29

Finding a wise group of experts in social networks

Current funding

  • 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

Before you email them, read our advice on how to contact a supervisor.

Available projects

  • Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    This project tackles the challenging problem of personalised predictive analytics with resource-constrained personal devices and massive-scale data. The knowledge to be generated concerns privacy, fairness, and resource efficiency in the era of Internet of Things. The expected outcomes include a collaborative learning paradigm for building personalised models on personal smart devices in open and fully decentralised settings. Privacy and model fairness are core tenets of the paradigm. Personalised predictive analytics is frontier research that will position Australia at the forefront of AI and give business the tools needed to deploy innovative business systems for market exploitation with a secure, equitable and competitive advantage.

    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

    Knowledge Graph-based Conversational Recommender Systems

    Principal Advisor

    Other advisors: Dr Miao Xu

  • Doctor Philosophy

    Image Generation from Texts

    Principal Advisor

    Other advisors: Dr Thomas Taimre, Dr Slava Vaisman

  • Doctor Philosophy

    Deep Learning for Graph Data Analysis

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Joint Feature Learning for Recommender System

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Federated Graph Neural Network-based Recommender Systems

    Principal Advisor

    Other advisors: Dr Miao Xu

  • Doctor Philosophy

    Meeting Challenges on Secure Recommender Systems

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Sustainable On-Device Recommender Systems

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Scalable and Generalizable Graph Neural Networks

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Understanding nitrous oxide emissions from wastewater treatment processes with stable isotopes and mathematical modelling

    Associate Advisor

    Other advisors: Dr Haoran Duan, Professor Liu Ye

  • Doctor Philosophy

    Lightweight Graph Neural Networks for Recommendation

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Integrated high-throughput material synthesis and characterisation system

    Associate Advisor

    Other advisors: Associate Professor Jingwei Hou

  • Doctor Philosophy

    Causal Analysis for Decision Support in Public Health

    Associate Advisor

    Other advisors: Professor Shazia Sadiq, Dr Rocky Chen

Completed supervision

Enquiries

For media enquiries about Professor Hongzhi Yin's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au