
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 (2022-2024) and 2024 Computer Science in Australia Leader Award, AI 2000 Most Influential Scholar Honorable Mention in Data Mining (2022-2024). 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 over 350+ papers with an H-index of 83 (22000+ citations), including 280+ CCF A/CORE A* and 70+ CCF B/CORE A, such as ICML, KDD, SIGIR, WWW, ACL, WSDM, SIGMOD, VLDB, ICDE, NeurIPS, 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 250+. 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 two PhD scholarships.
Latest News
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[23 May 2025] I was ranked #52 in Australia among Best Scientists for 2025 and have also been recognized with the Computer Science Leader Award for 2025 in Research.com.
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[15 May 2025] We have four research papers and one applied data science paper accepted by the top conference KDD 2025 (CORE A*, CCF A).
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Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation
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Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning
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FLUID-MMRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
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Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
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[11 May 2025] Our research work "RobGC: Towards Robust Graph Condensation" has been accepted by the top journal TKDE 2025 (CORE A*, CCF A). Congratulations to Xinyi.
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[1 May 2025] Our research work "Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective" has been accepted by the top conference ICML 2025 (CORE A*, CCF A). Congratulations to Hechuan.
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[4 April 2025] We have four full research papers accepted by the top conference SIGIR 2025 (CORE A*, CCF A).
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[2 April 2025] Congratulations to the four new doctors, Dr. Wei Yuan, Dr. Jing Long, Dr. Yuting Sun and Dr. Ruiqi Zheng, who were awarded their PhD by The University of Queensland.
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[10 March 2025] Our survey paper "A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security " has been accepted by TKDE 2025 (CORE A*, CCF A).
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[21 Feb 2025] Our joint foundation work "On the Trustworthiness of Generative Foundation Models– Guideline, Assessment, and Perspective" has been released on both arXiv and Hugging Face. This research is the result of a broad collaboration with leading universities and research institutions worldwide, including the University of Notre Dame, Massachusetts Institute of Technology, University of Waterloo, Carnegie Mellon University, University of Illinois Urbana-Champaign, Stanford University, University of California, Santa Barbara, IBM Research, Microsoft Research, The University of Queensland and more.
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[20 Feb 2025] I have been recognized as a Highly Ranked Scholar - Prior 5 Years (top 0.05% of all scholars) and #15 in Data Mining on ScholarGPS.
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[26 January 2025] Our survey paper "Graph Condensation: A Survey" has been accepted by TKDE 2025 (CORE A*, CCF A).
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[20 January 2025] We have three full research papers and one demo paper accepted by the top conference WWW 2025 (CORE A*, CCF A).
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Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
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BiasNavi: LLM-Empowered Data Bias Management
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[18 January 2025] We have two research papers accepted by AAAI 2025 (CCF A, CORE A*) for Oral Presentation.
Availability
- Professor Hongzhi Yin is:
- Available for supervision
Qualifications
- Postgraduate Diploma, Peking University
- Doctor of Philosophy, Peking University
Research interests
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Recommender System and User Modeling
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Graph Mining and Embedding
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Decentralized and Federated Learning
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Edge Machine Learning and Applications
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Trustworthy Machine Learning and Applications
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QA, Chatbot and Information Retrieval
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Time Series and Sequence Mining and Prediction
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Spatiotemporal Data Mining
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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
2016
Conference Publication
Expert team finding for review assignment
Yin, Hongzhi, Cui, Bin, Lu, Hua and Zhao, Lei (2016). Expert team finding for review assignment. Conference on Technologies and Applications of Artificial Intelligence (TAAI), Hsinchu, Taiwan, 25-27 November 2016. Piscataway, NJ, United States: IEEE. doi: 10.1109/TAAI.2016.7932314
2016
Conference Publication
LSIF: a system for large-scale information flow detection based on topic-related semantic similarity measurement
Zhao, Meng, Wang, Hao, Cao, Liangliang, Zhang, Chen, Yin, Hongzhi and Xu, Fanjiang (2016). LSIF: a system for large-scale information flow detection based on topic-related semantic similarity measurement. 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Singapore, 6-9 December 2016. Los Alamitos, CA United States: IEEE Computer Society. doi: 10.1109/WI-IAT.2015.2
2016
Book
Spatio-temporal recommendation in social media
Yin, Hongzhi and Cui, Bin (2016). Spatio-temporal recommendation in social media. Singapore: Springer Singapore. doi: 10.1007/978-981-10-0748-4
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Funding
Current funding
Past funding
Supervision
Availability
- Professor Hongzhi Yin is:
- Available for supervision
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Available projects
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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
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Doctor Philosophy
Decentralised Collaborative Predictive Analytics on Personal Smart Devices
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Joint Feature Learning for Recommender System
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Decentralised Collaborative Predictive Analytics on Personal Smart Devices
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Decentralised Collaborative Predictive Analytics on Personal Smart Devices
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Scalable and Lightweight On-Device Recommender Systems
Associate Advisor
Other advisors: Dr Rocky Chen, Dr Junliang Yu
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Doctor Philosophy
Scalable and Lightweight On-Device Recommender Systems
Associate Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Sustainable On-Device Recommender Systems
Associate Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Causal Analysis for Decision Support in Public Health
Associate Advisor
Other advisors: Professor Shazia Sadiq, Dr Rocky Chen
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Doctor Philosophy
Lightweight Graph Neural Networks for Recommendation
Associate Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Scalable and Generalizable Graph Neural Networks
Associate Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Integrated high-throughput material synthesis and characterisation system
Associate Advisor
Other advisors: Associate Professor Jingwei Hou
Completed supervision
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2025
Doctor Philosophy
Decentralized Learning for On-device Recommendation
Principal Advisor
Other advisors: Dr Rocky Chen
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2025
Doctor Philosophy
Deep Learning for Univariate Time Series Anomaly Detection in Industrial IoT
Principal Advisor
Other advisors: Dr Thomas Taimre, Dr Slava Vaisman
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2025
Doctor Philosophy
Secure Cross-device Federated Recommender Systems
Principal Advisor
Other advisors: Dr Miao Xu
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2025
Doctor Philosophy
Decentralized Point-Of-Interest (POI) Recommender Systems
Principal Advisor
Other advisors: Dr Rocky Chen
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2024
Doctor Philosophy
Federated Graph Neural Network-based Recommender Systems
Principal Advisor
Other advisors: Dr Miao Xu
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2023
Doctor Philosophy
From Cloud to Device: Transforming Recommender Systems for On-Device Deployment
Principal Advisor
Other advisors: Dr Miao Xu
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2023
Doctor Philosophy
Decentralized On-device Machine Learning and Unlearning for IoT Collaboration
Principal Advisor
Other advisors: Dr Miao Xu
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2023
Doctor Philosophy
Enhancing Recommender Systems wtih Self-Supervised Learning
Principal Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Secure Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Decentralized Framework for Embedding Large-scale Networks
Principal Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Toward Deep Conversational Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
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2021
Doctor Philosophy
Lightweight and Secure Deep Learning-based Mobile Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
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2020
Doctor Philosophy
Sequence Modelling for E-Commerce
Principal Advisor
Other advisors: Professor Xue Li
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2020
Doctor Philosophy
Graph Representation Learning with Attribute Information
Principal Advisor
Other advisors: Professor Xue Li
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2020
Master Philosophy
Advanced Machine Learning Algorithms for Discrete Datasets
Principal Advisor
Other advisors: Professor Shazia Sadiq
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2017
Doctor Philosophy
POINT OF INTERESTS RECOMMENDATION IN LOCATION-BASED SOCIAL NETWORKS
Principal Advisor
Other advisors: Professor Shazia Sadiq
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2025
Doctor Philosophy
Understanding and mitigating greenhouse gas emissions from wastewater system in the data era
Associate Advisor
Other advisors: Dr Haoran Duan, Professor Liu Ye
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2023
Doctor Philosophy
Multi-modal Data Modeling with Awareness of Efficiency, Reliability, and Privacy
Associate Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Neural Attentive Recommender Systems
Associate Advisor
Other advisors: Professor Helen Huang, Dr Rocky Chen
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2022
Master Philosophy
An exploration into the correlation between users' intentions and candidates for query- and non-query-based retrieval
Associate Advisor
Other advisors: Professor Helen Huang
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2021
Doctor Philosophy
Towards Efficient Similarity Search with Semantic Hashing Techniques
Associate Advisor
Other advisors: Professor Helen Huang
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2021
Doctor Philosophy
Multimedia Content Analytics with Modality Transition
Associate Advisor
Other advisors: Professor Helen Huang
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2018
Doctor Philosophy
Understand Video Event by Exploiting Semantic and Temporal Information for Classification and Retrieval
Associate Advisor
Other advisors: Professor Helen Huang
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Doctor Philosophy
Modelling Sequential Patterns of User Behaviour in Recommender Systems
Associate Advisor
Other advisors: Professor Helen Huang
Media
Enquiries
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