
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
2021
Conference Publication
DA-GCN: a domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation
Guo, Lei, Tang, Li, Chen, Tong, Zhu, Lei, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2021). DA-GCN: a domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. International Joint Conference on Artificial Intelligence, Montreal, Canada, 19-27 August 2021. San Francisco, CA, United States: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2021/342
2021
Conference Publication
Where are we in embedding spaces?
Zhang, Sixiao, Chen, Hongxu, Ming, Xiao, Cui, Lizhen, Yin, Hongzhi and Xu, Guandong (2021). Where are we in embedding spaces?. KDD '21: 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore, Singapore, 14 - 18 August 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3447548.3467421
2021
Conference Publication
Learning to ask appropriate questions in conversational recommendation
Ren, Xuhui, Yin, Hongzhi, Chen, Tong, Wang, Hao, Huang, Zi and Zheng, Kai (2021). Learning to ask appropriate questions in conversational recommendation. SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, 11-15 July 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3404835.3462839
2021
Conference Publication
Decoupling representation learning and classification for GNN-based anomaly detection
Wang, Yanling, Zhang, Jing, Guo, Shasha, Yin, Hongzhi, Li, Cuiping and Chen, Hong (2021). Decoupling representation learning and classification for GNN-based anomaly detection. International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual, 11-15 July 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3404835.3462944
2021
Conference Publication
Privacy protection in deep multi-modal retrieval
Zhang, Peng-Fei, Li, Yang, Huang, Zi and Yin, Hongzhi (2021). Privacy protection in deep multi-modal retrieval. 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual, 11-15 July 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3404835.3462837
2021
Conference Publication
Enhancing domain-level and user-level adaptivity in diversified recommendation
Liang, Yile, Qian, Tieyun, Li, Qing and Yin, Hongzhi (2021). Enhancing domain-level and user-level adaptivity in diversified recommendation. SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, 11-15 July 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3404835.3462957
2021
Journal Article
Utility mining across multi-dimensional sequences
Gan, Wensheng, Lin, Jerry Chun-Wei, Zhang, Jiexiong, Yin, Hongzhi, Fournier-Viger, Philippe, Chao, Han-Chieh and Yu, Philip S. (2021). Utility mining across multi-dimensional sequences. ACM Transactions on Knowledge Discovery from Data, 15 (5) 3446938, 1-24. doi: 10.1145/3446938
2021
Conference Publication
Self-supervised hypergraph convolutional networks for session-based recommendation
Xia, Xin, Yin, Hongzhi, Yu, Junliang, Wang, Qinyong, Cui, Lizhen and Zhang, Xiangliang (2021). Self-supervised hypergraph convolutional networks for session-based recommendation. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Virtual, 2-9 February 2021. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence.
2021
Conference Publication
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
Xia, Xin, Yin, Hongzhi, Yu, Junliang, Wang, Qinyong, Cui, Lizhen and Zhang, Xiangliang (2021). Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Online, 2–9 February 2021. Washington, DC United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v35i5.16578
Featured
2021
Conference Publication
Self-supervised multi-channel hypergraph convolutional network for social recommendation
Yu, Junliang, Yin, Hongzhi, Li, Jundong, Wang, Qinyong, Hung, Nguyen Quoc Viet and Zhang, Xiangliang (2021). Self-supervised multi-channel hypergraph convolutional network for social recommendation. WWW '21: Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19-23 April 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3442381.3449844
2021
Conference Publication
Adapting to context-aware knowledge in natural conversation for multi-turn response selection
Zhang, Chen, Wang, Hao, Jiang, Feijun and Yin, Hongzhi (2021). Adapting to context-aware knowledge in natural conversation for multi-turn response selection. WWW '21: Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19-23 April 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3442381.3449902
2021
Conference Publication
Multi-level hyperedge distillation for social linking prediction on sparsely observed networks
Sun, Xiangguo, Yin, Hongzhi, Liu, Bo, Chen, Hongxu, Meng, Qing, Han, Wang and Cao, Jiuxin (2021). Multi-level hyperedge distillation for social linking prediction on sparsely observed networks. WWW '21: Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19-23 April 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3442381.3449912
2021
Conference Publication
Graph embedding for recommendation against attribute inference attacks
Zhang, Shijie, Yin, Hongzhi, Chen, Tong, Huang, Zi, Cui, Lizhen and Zhang, Xiangliang (2021). Graph embedding for recommendation against attribute inference attacks. WWW '21: Proceedings of the Web Conference 2021, Ljubljana, Slovenia, 19-22 April 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3442381.3449813
2021
Conference Publication
Gallat: A spatiotemporal graph attention network for passenger demand prediction
Wang, Yuandong, Yin, Hongzhi, Chen, Tong, Liu, Chunyang, Wang, Ben, Wo, Tianyu and Xu, Jie (2021). Gallat: A spatiotemporal graph attention network for passenger demand prediction. 2021 IEEE 37th International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021. Washington, DC USA: IEEE Computer Society. doi: 10.1109/ICDE51399.2021.00212
2021
Conference Publication
Reliable recommendation with review-level explanations
Lyu, Yanzhang, Yin, Hongzhi, Liu, Jun, Liu, Mengyue, Liu, Huan and Deng, Shizhuo (2021). Reliable recommendation with review-level explanations. 2021 IEEE 37th International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021. Washington, DC USA: IEEE Computer Society. doi: 10.1109/ICDE51399.2021.00137
2021
Conference Publication
DDHH: A decentralized deep learning framework for large-scale heterogeneous networks
Imran, Mubashir, Yin, Hongzhi, Chen, Tong, Huang, Zi, Zhang, Xiangliang and Zheng, Kai (2021). DDHH: A decentralized deep learning framework for large-scale heterogeneous networks. 2021 IEEE 37th International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021. Washington, DC USA: IEEE Computer Society. doi: 10.1109/ICDE51399.2021.00196
2021
Conference Publication
Entity alignment for knowledge graphs with multi-order convolutional networks (extended abstract)
Tam, Nguyen Thanh, Trung, Huynh Thanh, Yin, Hongzhi, Van Vinh, Tong, Sakong, Darnbi, Zheng, Bolong and Hung, Nguyen Quoc Viet (2021). Entity alignment for knowledge graphs with multi-order convolutional networks (extended abstract). 2021 IEEE 37th International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021. Washington, DC USA: IEEE Computer Society. doi: 10.1109/ICDE51399.2021.00247
2021
Journal Article
Disease prediction via graph neural networks
Sun, Zhenchao, Yin, Hongzhi, Chen, Hongxu, Chen, Tong, Cui, Lizhen and Yang, Fan (2021). Disease prediction via graph neural networks. IEEE Journal of Biomedical and Health Informatics, 25 (3) 9122573, 818-826. doi: 10.1109/JBHI.2020.3004143
2021
Journal Article
An integrated model based on deep multimodal and rank learning for point-of-interest recommendation
Liao, Jianxin, Liu, Tongcun, Yin, Hongzhi, Chen, Tong, Wang, Jingyu and Wang, Yulong (2021). An integrated model based on deep multimodal and rank learning for point-of-interest recommendation. World Wide Web, 24 (2), 631-655. doi: 10.1007/s11280-021-00865-8
2021
Journal Article
Efficient and effective multi-modal queries through heterogeneous network embedding
Duong, Chi Thang, Nguyen, Tam Thanh, Yin, Hongzhi, Weidlich, Matthias, Mai, Son, Aberer, Karl and Nguyen, Quoc Viet Hung (2021). Efficient and effective multi-modal queries through heterogeneous network embedding. IEEE Transactions on Knowledge and Data Engineering, 34 (11), 1-1. doi: 10.1109/TKDE.2021.3052871
Funding
Current funding
Past funding
Supervision
Availability
- Professor Hongzhi Yin is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
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
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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2022
Doctor Philosophy
Secure 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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