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 did my PhD in computer science at UQ from 2019 to 2022, working with Helen Huang, Hongzhi Yin, and Zhiguo Yuan. I obtained my bachelor degree in electrical engineering at Beihang University (BUAA) from 2014-2018.

My research focuses on data science methods, including theory and application for various real-world scenarios, such as recommender systems, social network, urban computing, engineering, law, health etc.

I speak Cantonese, English, and Mandarin.

Recruitment: Open positions all year round!

Recent News

  • 08.2024 ARC DECRA project funded, “Lifelong Paradigms for Versatile, Robust and Agile Recommender Systems” (2025-2027)

  • 06.2024 Our team gives a talk, “Effective Representation Learning for Legal Case Retrieval”, at IR Seminar, the University of Glasgow. [slides]

  • 05.2024 Our team gives a talk, “Effective Representation Learning for Legal Case Retrieval”, at THUIR, Tsinghua University. [slides]

  • 03.2024 Give a talk, “Graph Learning Methods in Session-based Recommendations and Legal Case Retrieval”, at IRonGraphs Workshop at ECIR 2024. [slides]

  • 12.2023 Give a talk, “Graph Condensation for Continual Graph Learning”, at CSIRO.

  • 11.2023 Give a talk, “Graph Condensation for Continual Graph Learning”, at Artificial Intelligence Enabled Trustworthy Recommendations Workshop at AJCAI 2023. [slides]

  • 11.2023 Best Paper Award at ADC 2023 with my student, Yan!

  • 08.2023 Give a talk, “Recent Advances of Data Science Methods in Public Health”, at ICIAM, Busan.

  • 07.2023 Winner of Task 2 and 4 at Social Media Mining for Health Competition (SMM4H) 2023!

  • 11.2022 Give a talk, “Item- and Sequence-level Contrastive Learning in Sequential Recommendation” at TIGER Seminar at RMIT.

  • 06.2022 Give a talk, “Item- and Sequence-level Contrastive Learning in Sequential Recommendation” at IR Seminar at the University of Glasgow.

  • 12.2021 ACM MM Asia 2021 PhD Lightning Talk Award, Highly Commended.

  • 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

PontTuset 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.

PontTuset 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.

PontTuset 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.

PontTuset 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.

PontTuset 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.

PontTuset 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

PontTuset 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).

PontTuset 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.

PontTuset 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.

PontTuset 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).

PontTuset 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.

PontTuset 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.

PontTuset 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.

PontTuset 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

PontTuset 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
PDF

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)
  • 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 –>

Updated on 25/08/2024.