Ruihong Qiu
邱瑞鸿 /`ray hong chill/
I am currently a Lecturer/Assistant Professor and an ARC DECRA fellow (2025-2027) in School of Electrical Engineering and Computer Science (EECS) at The University of Queensland (UQ). I completed my PhD in Computer Science at UQ from 2019 to 2022 and subsequently worked as a Postdoctoral Research Fellow from 2022 to 2024, collaborating with Helen Huang, Hongzhi Yin, and Zhiguo Yuan. Before this, I obtained my bachelor’s degree in Electrical Engineering at Beihang University (BUAA) from 2018.
My research focuses on data science methods, including theory and application, such as graph neural networks, information retrieval, recommender systems, time series etc.:
- Graph Neural Networks. Text-attributed graph and multimodal graph with (M)LLMs; Graph foundation models; Robustness and scalability of graph learning (OOD, test-time, condensation, distillation, graph-MLP etc.).
- Recommendations and Information Retrieval. RL for recommendations; Rich side-information in recommendations with (M)LLMs; Domain-specific IR and document understanding with LLMs.
- Time Series. Foundation models for time series; LLMs for time series; Time series for cross-disciplinary applications.
Recruitment
I am actively looking for (3-5) self-motivated PhD students in Year 2025. All fully funded! [For prospective PhD students], [博士招生中文], [For master theses/bachelor honours students at UQ] and [For interns/visitors].
Recent News
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08.2024 ARC DECRA project funded, “Lifelong Paradigms for Versatile, Robust and Agile Recommender Systems” (2025-2027)
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06.2024 Our team gives a talk, “Effective Representation Learning for Legal Case Retrieval”, at IR Seminar, the University of Glasgow. [slides]
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05.2024 Our team gives a talk, “Effective Representation Learning for Legal Case Retrieval”, at THUIR, Tsinghua University. [slides]
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03.2024 Give a talk, “Graph Learning Methods in Session-based Recommendations and Legal Case Retrieval”, at IRonGraphs Workshop at ECIR 2024. [slides]
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12.2023 Give a talk, “Graph Condensation for Continual Graph Learning”, at CSIRO.
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11.2023 Give a talk, “Graph Condensation for Continual Graph Learning”, at Artificial Intelligence Enabled Trustworthy Recommendations Workshop at AJCAI 2023. [slides]
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11.2023 Best Paper Award at ADC 2023 with my student, Yan!
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08.2023 Give a talk, “Recent Advances of Data Science Methods in Public Health”, at ICIAM, Busan.
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07.2023 Winner of Task 2 and 4 at Social Media Mining for Health Competition (SMM4H) 2023!
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11.2022 Give a talk, “Item- and Sequence-level Contrastive Learning in Sequential Recommendation” at TIGER Seminar at RMIT.
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06.2022 Give a talk, “Item- and Sequence-level Contrastive Learning in Sequential Recommendation” at IR Seminar at the University of Glasgow.
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12.2021 ACM MM Asia 2021 PhD Lightning Talk Award, Highly Commended.
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07.2020 3MT competition Runner-up and People’s Choice Awards at ITEE [video].
Selected Research
Google Scholar page includes the full publication list.
Graph Condensation
GCondenser: Benchmarking Graph Condensation
Yilun Liu, Ruihong Qiu, Zi Huang Preprint arXiv / code We introduce a benchmark for graph condensation with a thorough methodology development method and an extensive evaluation protocol. |
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PUMA: Efficient Continual Graph Learning with Graph Condensation
Yilun Liu, Ruihong Qiu, Yanran Tang, Hongzhi Yin, Zi Huang TKDE 2024 (major revision) arXiv / code We extend the Condense-and-Train (CaT) continual graph learning algorithm with a more efficient and effective, psudo-label guided memory bank (PUMA🐆) framework. |
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CaT: Balanced Continual Graph Learning with Graph Condensation
Yilun Liu, Ruihong Qiu, Zi Huang ICDM 2023 arXiv / code We introduce a Condense-and-Train (CaT🐱) memory-based continual graph learning algorithm using graph condensation to construct a more representative memory bank. And a Train-in-Memory continual learning scheme can further alleviate the imbalanced training issue in Class Incremental Learning. |
Legal Case Retrieval
CaseLink: Inductive Graph Learning for Legal Case Retrieval
Yanran Tang, Ruihong Qiu, Hongzhi Yin, Xue Li, Zi Huang SIGIR 2024 arXiv / code We introduce an inductive graph learning paradigm for legal case retrieval to tackle the challenge of unseen testing query and candidate cases. |
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CaseGNN++: Graph Contrastive Learning for Legal Case Retrieval with Graph Augmentation
Yanran Tang, Ruihong Qiu, Yilun Liu, Xue Li, Zi Huang TOIS 2024 (under review) arXiv / code We introduce an extended CaseGNN++ method with graph augmentations based on the CaseGNN framework. |
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CaseGNN: Graph Neural Networks for Legal Case Retrieval with Text-Attributed Graphs
Yanran Tang, Ruihong Qiu, Yilun Liu, Xue Li, Zi Huang ECIR 2024 arXiv / code We introduce a structural modelling of law case for effective retrieval with the aid of summarisation from LLM and grpah neural networks. |
Recommender Systems
Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation
Ruihong Qiu, Zi Huang, Hongzhi Yin ICDM 2022 arXiv / code / video We introduce shared input-output embedding Recurrent Neural Tangent Kernel to sequential recommendation (OverRec). |
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Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
Ruihong Qiu, Zi Huang, Hongzhi Yin, Zijian Wang WSDM 2022 arXiv / code We discover and find the cause of representation degeneration problem in sequential recommendation (DuoRec). A contrastive learning regularisation is applied to enforce the distribution to be uniform. |
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Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation
Ruihong Qiu, Zi Huang, Hongzhi Yin ICDM 2021 arXiv / code / video We introduce multi-instance NCE loss to enhance the side-information based item representation learning (MMInfoRec) in sequential recommendation. |
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CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
Ruihong Qiu, Sen Wang, Zhi Chen, Hongzhi Yin, Zi Huang ACM MM 2021 (oral) arXiv / code / video We introduce a structural causal graph to debias the visual bias in item recommendation (CausalRec). |
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Exploiting Positional Information for Session-based Recommendation
Ruihong Qiu, Zi Huang, Tong Chen, Hongzhi Yin TOIS 2021 arXiv We introduce a dual positional encoding to theoretically characterise and represent the positional information (PosRec) in session-based recommendation. |
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GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
Ruihong Qiu, Hongzhi Yin, Zi Huang, Tong Chen SIGIR 2020 arXiv / code / video We introduce a global attributed graph (GAG) neural network for streaming session-based recommendation. |
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Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks
Ruihong Qiu, Jingjing Li, Zi Huang, Hongzhi Yin TOIS 2020 arXiv We introduce a global graph to model cross session information in session-based recommendation. |
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Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
Ruihong Qiu, Jingjing Li, Zi Huang, Hongzhi Yin CIKM 2019 arXiv / code We introduce a Full Graph Neural Network (FGNN) for to model a session as graph in session-based recommendation. |
Water Management with Data Science
An integrated first principal and deep learning approach for modeling nitrous oxide emissions from wastewater treatment plants
Kaili Li, Haoran Duan, Linfeng Liu, Ruihong Qiu, Ben van den Akker, Bing-Jie Ni, Tong Chen, Hongzhi Yin, Zhiguo Yuan, Liu Ye Environmental Science & Technology 2022 We use sequential modelling in deep learning to predict the amount of emitted N2O with green-house effect. |
Team
- Boyu Luo, UQ EECS PhD (7.2024-, co-advise with Helen Huang and Guangdong Bai)
- Yan Jiang, UQ EECS PhD (1.2024-, co-advise with Helen Huang and Guangdong Bai)
- Danny Wang, UQ EECS PhD (1.2024-, co-advise with Helen Huang and Guangdong Bai)
- Hrishikesh Patel, UQ EECS PhD (4.2023, co-advise with Sen Wang)
- Yi Zhang, UQ EECS PhD (4.2023-, co-advise with Sen Wang and Jiajun Liu)
- Yilun Liu, UQ EECS PhD (1.2023-, co-advise with Helen Huang)
- Jingyu Ge, UQ ACWEB PhD (1.2022-, co-advise with Zhiguo Yuan, Helen Huang, and Jiuling Li)
Service
- Conference organisation: PhD Symposium Co-chair at WWW’25, Program Committee Co-chair at ADC’23, PhD Forum Co-Chair at AJCAI’23 <!– * Reviewer: TKDE, TNNLS, TOIS, TPAMI, WWWJ
- PC member: ACML’20, AJCAI’23, CIKM’24’23’22’21’20, DASFAA’24’23, ICDE’20, ICDM’22’21, ICMR’23, IJCAI’24’23’20, SIGIR’24’23’20’19, SIGIR-AP’23, SIGMOD’20, VLDB’22’21, WSDM’23’22’21, WWW’25 –>
MISC
I speak Cantonese, Mandarin and English.
Updated on 29/10/2024.