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Professor Hongzhi Yin
Professor

Hongzhi Yin

Email: 
Phone: 
+61 7 336 54739

Overview

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.

Works

Search Professor Hongzhi Yin’s works on UQ eSpace

338 works between 2011 and 2024

221 - 240 of 338 works

2019

Journal Article

Online sales prediction via trend alignment-based multitask recurrent neural networks

Chen, Tong, Yin, Hongzhi, Chen, Hongxu, Wang, Hao, Zhou, Xiaofang and Li, Xue (2019). Online sales prediction via trend alignment-based multitask recurrent neural networks. Knowledge and Information Systems, 62 (6), 2139-2167. doi: 10.1007/s10115-019-01404-8

Online sales prediction via trend alignment-based multitask recurrent neural networks

2019

Journal Article

Group-level personality detection based on text generated networks

Sun, Xiangguo, Liu, Bo, Meng, Qing, Cao, Jiuxin, Luo, Junzhou and Yin, Hongzhi (2019). Group-level personality detection based on text generated networks. World Wide Web, 23 (3), 1887-1906. doi: 10.1007/s11280-019-00729-2

Group-level personality detection based on text generated networks

2019

Journal Article

Semi-supervised clustering with deep metric learning and graph embedding

Li, Xiaocui, Yin, Hongzhi, Zhou, Ke and Zhou, Xiaofang (2019). Semi-supervised clustering with deep metric learning and graph embedding. World Wide Web, 23 (2), 781-798. doi: 10.1007/s11280-019-00723-8

Semi-supervised clustering with deep metric learning and graph embedding

2019

Journal Article

Efficient user guidance for validating participatory sensing data

Cong, Phan Thanh, Tam, Nguyen Thanh, Yin, Hongzhi, Zheng, Bolong, Stantic, Bela and Hung, Nguyen Quoc Viet (2019). Efficient user guidance for validating participatory sensing data. ACM Transactions on Intelligent Systems and Technology, 10 (4) 37, 1-30. doi: 10.1145/3326164

Efficient user guidance for validating participatory sensing data

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?. The Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, HI United States, 27 January – 1 February 2019. Menlo Park, CA United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v33i01.330110031

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

2019

Book Chapter

Spatiotemporal recommendation with big geo-social networking data

Wang, Weiqing and Yin, Hongzhi (2019). Spatiotemporal recommendation with big geo-social networking data. Big data recommender systems - Volume 1: Algorithms, architectures, big data, security and trust. (pp. 193-224) edited by Osman Khalid, Samee U. Khan and Albert Y. Zomaya. Stevenage, United Kingdom: The Institution of Engineering and Technology. doi: 10.1049/pbpc035f_ch9

Spatiotemporal recommendation with big geo-social networking data

2019

Journal Article

MCP: a multi-component learning machine to predict protein secondary structure

Khalatbari, Leila, Kangavari, M. R., Hosseini, Saeid, Yin, Hongzhi and Cheung, Ngai-Man (2019). MCP: a multi-component learning machine to predict protein secondary structure. Computers in Biology and Medicine, 110, 144-155. doi: 10.1016/j.compbiomed.2019.04.040

MCP: a multi-component learning machine to predict protein secondary structure

2019

Journal Article

Leveraging multi-aspect time-related influence in location recommendation

Hosseini, Saeid, Yin, Hongzhi, Zhou, Xiaofang, Sadiq, Shazia, Kangavari, Mohammad Reza and Cheung, Ngai-Man (2019). Leveraging multi-aspect time-related influence in location recommendation. World Wide Web, 22 (3), 1001-1028. doi: 10.1007/s11280-018-0573-2

Leveraging multi-aspect time-related influence in location recommendation

2019

Journal Article

Spatiotemporal representation learning for translation-based POI recommendation

Qian, Tieyun, Liu, Bei, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2019). Spatiotemporal representation learning for translation-based POI recommendation. ACM Transactions on Information Systems, 37 (2) 18, 1-24. doi: 10.1145/3295499

Spatiotemporal representation learning for translation-based POI recommendation

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

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

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?

2019

Conference Publication

Inferring substitutable products with deep network embedding

Zhang, Shijie, Yin, Hongzhi, Wang, Qinyong, Chen, Tong, Chen, Hongxu and Nguyen, Quoc Viet Hung (2019). Inferring substitutable products with deep network embedding. International Joint Conference on Artificial Intelligence, Macao, China, 10-16 August 2019. California: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2019/598

Inferring substitutable products with deep network embedding

2019

Conference Publication

Multi-hop path queries over knowledge graphs with neural memory networks

Wang, Qinyong, Yin, Hongzhi, Wang, Weiqing, Huang, Zi, Guo, Guibing and Nguyen, Quoc Viet Hung (2019). Multi-hop path queries over knowledge graphs with neural memory networks. 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22 - 25 April 2019. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-18576-3_46

Multi-hop path queries over knowledge graphs with neural memory networks

2019

Conference Publication

Enhancing collaborative filtering with generative augmentation

Wang, Qinyong, Nguyen, Quoc Viet Hung, Yin, Hongzhi, Huang, Zi, Wang, Hao and Cui, Lizhen (2019). Enhancing collaborative filtering with generative augmentation. 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: 10.1145/3292500.3330873

Enhancing collaborative filtering with generative augmentation

2019

Journal Article

Spatiotemporal recommendation with big geo-social networking data

Wang, Weiqing and Yin, Hongzhi (2019). Spatiotemporal recommendation with big geo-social networking data. IET Professional Applications of Computing Series, 35, 193-224.

Spatiotemporal recommendation with big geo-social networking data

Funding

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

Supervision

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

    Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Meeting Challenges on Secure Recommender Systems

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Image Generation from Texts

    Principal Advisor

    Other advisors: Dr Thomas Taimre, Dr Slava Vaisman

  • Doctor Philosophy

    Knowledge Graph-based Conversational Recommender Systems

    Principal Advisor

    Other advisors: Dr Miao Xu

  • 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

    Sustainable On-Device Recommender Systems

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Causal Analysis for Decision Support in Public Health

    Associate Advisor

    Other advisors: Professor Shazia Sadiq, Dr Rocky Chen

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Lightweight Graph Neural Networks for Recommendation

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Scalable and Generalizable Graph Neural Networks

    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

    Scalable and Lightweight On-Device Recommender Systems

    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

Completed supervision

Media

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