CityU

Assistant Professor Shiqing Wu


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/

 

Educational 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

 

Incumbent

Assistant Professor, Faculty of Data Science, City University of Macau

 

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.

 

Teaching Experience

Information Security and Management, University of Technology Sydney, Sydney, Australia
Cloud Computing Foundations, TAFE NSW, Sydney, Australia
Machine Learning, University of Southern Queensland, Sydney, Australia
Programming for Intelligent Web Services and Applications, University of Tasmania, Hobart, Australia
Data Analytics, University of Tasmania, Hobart, Australia
Program Design and Construction/Software Construction, 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 and publication

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.