Dr. Shiqing Wu

Assistant Professor Master Supervisor

Email: sqwu@cityu.edu.mo

Tel: (853)85902374

Office address: Room S504, Stanley Ho Building, City University of Macau (Taipa)

Personal homepage: https://shiqingwu.site/

Academic Qualifications

2022 | Doctor of Philosophy, University of Tasmania, Australia
2016 | Bachelor of Computer and Information Sciences, Auckland University of Technology, New Zealand
2016 | Bachelor of Computer Science and Technology, China Jiliang University, China

Working Experience

12/2024–Present | Assistant Professor, Faculty of Data Science, City University of Macau, Macao SAR, China
07/2022–12/2024 | Postdoctoral Research Fellow, School of Computer Science, University of Technology Sydney, Sydney, Australia
07/2024–12/2024 | Academic Tutor, School of Computer Science, University of Technology Sydney, Sydney, Australia
02/2024–11/2024 | Lecturer, TAFE NSW, Sydney, Australia
11/2022–02/2023 | Lecturer, University of Southern Queensland Sydney Center, Sydney, Australia.
02/2020–07/2022 | Academic Tutor, School of Information and Communication Technology, University of Tasmania, Hobart, Australia.
07/2017–11/2019 | Teaching Assistant, School of Engineering, Computer and Mathematical Sciences,
Auckland University of Technology, Auckland, New Zealand.
02/2017–04/2017 | Research Assistant, School of Engineering, Computer and Mathematical Sciences,
Auckland University of Technology, Auckland, New Zealand.

Research Interests

Artificial Intelligence

Social Influence Analysis

Multi-agent Systems

Recommender Systems

Reinforcement Learning, and Graph Neural Networks.

Research & Publications

Refereed Conference Papers

[C14] Shanshan Sui, Qilong Han, Dan Lu, Shiqing Wu, and Guandong Xu. (2024). “Enhancing Traffic Prediction via Spatial Multi-granularity Co-evolving Mechanism”. In: The 11th International Conference on Behavior, Economic and Social Computing (BESC).

[C13] Dan Lu, Xu Chen, Rui Chen, Shiqing Wu, and Guandong Xu. (2024). “Fairness-aware Mutual Information Multimodal Recommendation”. In: The 11th International Conference on Behavior, Economic and Social Computing (BESC). (Best Paper Award)

[C12] Shiqing Wu and Guandong Xu. (2024). “Learning Influential Relationships for Implicit Influence Maximization in unknown Networks”. In: The 11th International Conference on Behavior, Economic and Social Computing (BESC).

[C11] Dan Lu, Hao Zhang, Lijie Li, Shiqing Wu, and Guandong Xu. (2024). “Cascading Hypergraph Convolution Networks for Mutli-Behavior Sequential Recommendation”. In: The 11th International Conference on Behavior, Economic and Social Computing (BESC).

[C10] Qilong Han, Shanshan Sui, Dan Lu, Shiqing Wu, and Guandong Xu. (2024). “Enhancing Spatiotemporal Prediction with Intra- and Inter-granularity Contrastive Learning”. In: The 29th International Conference on Database Systems for Advanced Applications (DASFAA). (CORE-B, CCF-B).

[C9] Shiqing Wu, Guandong Xu, and Xianzhi Wang. (2023). “SOAC: Supervised Off-Policy Actor-Critic for Recommender Systems”. In: The 23rd IEEE International Conference on Data Mining (ICDM). (CORE-A*, CCF-B).

[C8] Kangzheng Liu, Feng Zhao, Guandong Xu, and Shiqing Wu. (2023). “IE-Evo: Internal and External Evolution-Enhanced Temporal Knowledge Graph Forecasting”. In: The 23rd IEEE International Conference on Data Mining (ICDM). (CORE-A*, CCF-B).

[C7] Guan Wang, Weihua Li, Shiqing Wu, Quan Bai, and Edmund Lai. (2023). “BeECD: Belief-aware Echo Chamber Detection over Twitter Stream”. In: The 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI). (CORE-B, CCF-C).

[C6] Haoran Tang, Shiqing Wu, Guandong Xu, and Qing Li. (2023). “Dynamic Graph Evolution Learning for Recommendation”. In: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). (CORE-A*, CCF-A).

[C5] Yuxuan Hu, Shiqing Wu, Chenting Jiang, Weihua Li, Quan Bai, and Erin Roehrer. (2022). “AI Facilitated Isolations? The Impact of Recommendation-based Influence Diffusion in Human Society”. In: The 31st International Joint Conference on Artificial Intelligence (IJCAI). (CORE-A*, CCF-A).

[C4] Chenting Jiang, Weihua Li, Shiqing Wu, and Quan Bai. (2021). “OMT: An Operate-based Approach for Modelling Multi-topic Influence Diffusion in Online Social Networks”. In: The 22nd International Conference on Web Information Systems Engineering (WISE). (CORE-A, CCF-C).

[C3] Shiqing Wu, Quan Bai, and Weihua Li. (2021). “Learning Policies for Effective Incentive Allocation in Unknown Social Networks”. In: The 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). (CORE-A*, CCF-B).

[C2] Shiqing Wu and Quan Bai. (2019). “Incentivizing Long-term Engagement Under Limited Budget”. In: The 16th Pacific Rim International Conference on Artificial Intelligence (PRICAI). (CORE-B, CCF-C).

[C1] Shiqing Wu, Quan Bai, and Byeong Ho Kang. (2019). “Adaptive Incentive Allocation for Influence-aware Proactive Recommendation”. In: The 16th Pacific Rim International Conference on Artificial Intelligence (PRICAI). (CORE-B, CCF-C).

Refereed Journal Papers

[J10] Shanshan Sui, Qilong Han, Dan Lu, Shiqing Wu, and Guandong Xu. (2024). “A novel complex network prediction method based on multi-granularity contrastive learning”. In: CCF Transactions on Pervasive Computing and Interaction. (SJR-Q2, JCR-Q3, IF 2.2).

[J9] Haoran Tang, Shiqing Wu, Xueyao Sun, Jun Zeng, Guandong Xu, and Qing Li. (2024). “TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic Recommendation”. In: ACM Transactions on Information Systems. (SJR-Q1, JCR-Q1, CCF-A, IF 5.4).

[J8] Mengyan Wang, Yuxuan Hu, Shiqing Wu, Weihua Li, Quan Bai, Zihan Yuan, and Chenting Jiang. (2024). “Nudging Towards Responsible Recommendations: A Graph-Based Approach to Mitigate Belief Filter Bubbles”. In: IEEE Transactions on Artificial Intelligence. (SJR-Q1).

[J7] Mengyan Wang, Weihua Li, Jingli Shi, Shiqing Wu, and Quan Bai. (2023). “DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems”. In: Applied Intelligence. (SJR-Q2, JCR-Q2, CCF-C, IF 3.4).

[J6] Weihua Li, Yuxuan Hu, Chenting Jiang, Shiqing Wu, Quan Bai, and Lai Edmund. (2023). “ABEM: An Adaptive Agent-based Evolutionary Approach for Influence Maximization in Dynamic Social Networks”. In: Applied Soft Computing. (SJR-Q1, JCR-Q1, IF 7.2).

[J5] Shiqing Wu, Weihua Li, Shen Hao, and Quan Bai. (2023). “Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction”. In: Information Sciences. (SJR-Q1, JCR-Q1, CCF-B, IF 8.1).

[J4] Xiang Li, Qing Liu, Shiqing Wu, Zehong Cao, and Quan Bai. (2023). “Game Theory Based Compatible Incentive Mechanism Design for Non-Cryptocurrency Blockchain Systems”. In: Journal of Industrial Information Integration. (SJR-Q1, JCR-Q1, IF 10.4).

[J3] Shiqing Wu, Weihua Li, and Quan Bai. (2023). “GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks”. In: Knowledge-Based Systems. (SJR-Q1, JCR-Q1, CCF-C, IF 7.2).

[J2] Chenting Jiang, Anthony D’Arienzo, Weihua Li, Shiqing Wu, and Quan Bai. (2021). “An Operator-based Approach for Modeling Influence Diffusion in Complex Social Networks”. In: Journal of Social Computing. (SJR-Q2).

[J1] Shiqing Wu, Quan Bai, and Sotsay Sengvong. (2018). “Greencommute: An Influence-aware Persuasive Recommendation Approach for Public-friendly Commute Options”. In: Journal of Systems Science and Systems Engineering. (SJR-Q2, JCR-Q3, IF 1.7).

Selected Awards

• Best Paper Award for the paper entitled “Fairness-aware Mutual Information Multimodal Recommendation” at BESC 2024, 2024.
• AAMAS 2021 Student Scholarship, IFAAMAS, 2021.
• Ph.D. Scholarship, University of Tasmania, 2019-2022.
• Ph.D. Scholarship, Auckland University of Technology, 2018-2019.
• Postgraduate Scholarship, Auckland University of Technology, 2016-2018.