邱瑞鸿 /`ray hong chill/

I am currently an Associate Lecturer and a Postdoctoral Research Fellow at The University of Queensland (UQ). I did my PhD in computer science from 2019 to 2022 at UQ, working with Helen Huang, Hongzhi Yin, and Zhiguo Yuan.

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

• 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.

	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.
	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.
	CaT: Balanced Continual Graph Learning with Graph Condensation Yilun Liu, Ruihong Qiu, Zi Huang ICDM 2023 arXiv / code We introduce a 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 (CaT🐱).
	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).
	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.
	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.
	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.
	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).
	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.
	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.
	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.
	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.

Team

• 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)
• Van Nhat Huy Nguyen, UQ EECS PhD (4.2023-, co-advise with Sen Wang)
• 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)

Teaching

• Social Analytics, UQ BSAN7207 (Coordinator and Lecturer, Sem 1, 2024)
• Introduction to Data Science, UQ DATA7001 (Co-Coordinator and Lecturer, Sem 2, 2023 with Student Evaluation 4.6/5; Sem 1, 2024)

Updated on 27/3/2024.