Publications

Full collection of publications on Google Scholar.

[18] Yilun Liu*, Ruihong Qiu, Yanran Tang, Zi Huang, Hongzhi Yin, PUMA: Efficient Continual Graph Learning with Graph Condensation, in IEEE Transactions on Knowledge and Data Engineering (TKDE) 2024 (Q1, under review)

[17] Yanran Tang*, Ruihong Qiu, Yilun Liu, Xue Li and Zi Huang, CaseGNN: Graph Neural Networks for Legal Case Retrieval with Text-Attributed Graphs, in European Conference on Information Retrieval (ECIR) 2024 (CORE A)

[16] Anjie Zhu*, Peng-Fei Zhang, Ruihong Qiu, Zetao Zheng, Zi Huang, Jie Shao, Abstract and Explore: A Novel Behavioral Metric with Cyclic Dynamics in Reinforcement Learning, in Annual AAAI Conference on Artificial Intelligence (AAAI) 2024 (CORE A*)

[15] Jiasheng Duan, Peng-Fei Zhang, Ruihong Qiu, Zi Huang, Long short-term enhanced memory for sequential recommendation, in World Wide Web 2023

[14] Yan Jiang*, Ruihong Qiu, Yi Zhang, Peng-Fei Zhang, Balanced and Explainable Social Media Analysis for Public Health with Large Language Models, in Australasian Database Conference (ADC) 2023 (Best Paper Award, Competition Winner 1/29 of Social Media Mining for Health (SMM4H))

[13] Yilun Liu*, Ruihong Qiu, Zi Huang, CaT: Balanced Continual Graph Learning with Graph Condensation, in IEEE International Conference on Data Mining (ICDM) 2023 (CORE A*)

[12] Kaili Li, Haoran Duan, Linfeng Liu, Ruihong Qiu, Ben van den Akker, Bing-Jie Ni, Tong Chen, Hongzhi Yin, Zhiguo Yuan, Liu Ye, An Integrated First Principal and Deep Learning Approach for Modeling Nitrous Oxide Emissions from Wastewater Treatment Plants, in Environmental Science & Technology (EST) 2022 (Q1)

[11] Ruihong Qiu, Zi Huang, Hongzhi Yin, Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation, in IEEE International Conference on Data Mining (ICDM) 2022 (CORE A*, acceptance rate 9.8%)

[10] Ruihong Qiu, Zi Huang, Hongzhi Yin, Zijian Wang, Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation, in ACM International Conference on Web Search and Data Mining (WSDM) 2022 (CORE A*)

[9] Ruihong Qiu, Zi Huang, Chen Tong, Hongzhi Yin, Exploiting Positional Information for Session-based Recommendation, in ACM Transactions on Information Systems (TOIS) 2021 (Q1)

[8] Ruihong Qiu, Zi Huang, Hongzhi Yin, Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation, in IEEE International Conference on Data Mining (ICDM) 2021 (CORE A*, acceptance rate 9.9%)

[7] Zhi Chen, Yadan Luo, Ruihong Qiu, Sen Wang, Zi Huang, Jingjing Li, Zheng Zhang, Semantics Disentangling for Generalized Zero-Shot Learning, in International Conference on Computer Vision (ICCV) 2021 (CORE A*)

[6] Zijian Wang, Yadan Luo, Ruihong Qiu, Zi Huang, Mahsa Baktashmotlagh, Learning to Diversify for Single Domain Generalization in International Conference on Computer Vision (ICCV) 2021 (CORE A*)

[5] Ruihong Qiu, Sen Wang, Zhi Chen, Hongzhi Yin, Zi Huang, CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation, in ACM International Conference on Multimedia (MM) 2021 (CORE A*, oral)

[4] Zhi Chen, Yadan Luo, Sen Wang, Ruihong Qiu, Jingjing Li, Zi Huang, Enhancing Generalized Zero-Shot Learning by Visual Perturbation, in ACM International Conference on Multimedia (MM) 2021 (CORE A*)

[3] Ruihong Qiu, Jingjing Li, Zi Huang, Hongzhi Yin, Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks, in ACM Transactions on Information Systems (TOIS) 2020 (Q1)

[2] Ruihong Qiu, Hongzhi Yin, Zi Huang, Tong Chen, GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation, in International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) 2020 (CORE A*)

[1] Ruihong Qiu, Jingjing Li, Zi Huang, Hongzhi Yin, Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks, in ACM International Conference on Information and Knowledge Management (CIKM) 2019 (CORE A)

* indicates student author(s) mentored by me.