吳世卿 博士
助理教授 碩導
學校郵箱：sqwu@cityu.edu.mo
辦公電話：(853)85902374
辦公地址：澳門城市大學(氹仔)何鴻燊樓S504室
個人主頁： https://shiqingwu.site/
學歷
2022 | 計算機科學博士，塔斯馬尼亞大學，澳大利亞
2016 | 計算機和信息科學學士，奧克蘭理工大學，新西蘭
2016 | 計算機科學與技術學士，中國計量大學，中國
現任
澳門城市大學數據科學學院助理教授
工作經歷
12/2024–至今 | 助理教授 (Assistant Professor)，數據科學學院，澳門城市大學，中國澳門
07/2022–12/2024 | 博士後研究員 (Postdoctoral Research Fellow)，計算機科學學院，悉尼科技大學，澳大利亞悉尼
07/2024–12/2024 | 助教 (Academic Tutor)，計算機科學學院，悉尼科技大學，澳大利亞悉尼
02/2024–11/2024 | 講師 (Lecturer)，新南威爾士公立技術學院（TAFE NSW），澳大利亞悉尼
11/2022–02/2023 | 講師 (Lecturer)，南昆士蘭大學悉尼中心，澳大利亞悉尼
02/2020–07/2022 | 助教 (Academic Tutor)，信息與通信技術學院，塔斯馬尼亞大學，澳大利亞霍巴特
07/2017–11/2019 | 助教 (Teaching Assistant)，工程計算機與數學科學學院，奧克蘭理工大學，新西蘭奧克蘭
02/2017–04/2017 | 研究助理 (Research Assistant)，工程計算機與數學科學學院， 奧克蘭理工大學，新西蘭奧克蘭
曾任教科目
信息安全與管理（Information Security and Management），悉尼科技大學，澳大利亞悉尼
雲計算基礎（Cloud Computing Foundations），新南威爾士公立技術學院（TAFE NSW），澳大利亞悉尼
機器學習（Machine Learning），南昆士蘭大學，澳大利亞悉尼
智能Web服務與應用編程（Programming for Intelligent Web Services and Applications），塔斯馬尼亞大學，澳大利亞霍巴特
數據分析（Data Analytics），塔斯馬尼亞大學，澳大利亞霍巴特
程序設計與構建/軟件構建（Program Design and Construction/Software Construction），奧克蘭理工大學，新西蘭奧克蘭
研究方向
人工智能，多智能體建模，社會影響力分析，推薦系統，強化學習，圖神經網絡
研究及出版
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).
學術獎項
- 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.
