Profile of Prof XU, Guandong 徐貫東 - The Education University of Hong Kong


Chair Professor	Office of the President
Director	University Research Facility of Data Science and Artificial Intelligence
Director	Centre for Learning, Teaching and Technology

Contact

ORCiD

0000-0003-4493-6663

Phone

(852) 2948 8818

Fax

(852) 2948 6314 / (852) 2948 7046

gdxu@eduhk.hk

Address

10 Lo Ping Road, Tai Po, New Territories, Hong Kong

Website

https://pappl.eduhk.hk/rich/web/person.xhtml?pid=308319&name=XU, Guandong

Scopus ID

8987733300

SDGs

3 - Good Health and Well-Being
4 - Quality Education
5 - Gender Equality
9 - Industry, Innovation and Infrastructure
10 - Reduced Inequality
11 - Sustainable Cities and Communities

Research Outputs


Scholarly Books, Monographs and Chapters Xu, G., & Fang, B. (2025). The Application of MDATA Cognitive Model in Network Public Opinion Analysis. In, Y. Jia, Z. Gu, A. Li, & B. Fang (Eds.), MDATA Cognitive Model: Theory and Applications (pp. 181-207). Singapore: Springer. https://doi.org/10.1007/978-981-96-3528-3_7
Journal Publications Lu, D., Wu, S., Zhang, H., Xu, G., & Han, Q. (2025). Causal Cascading Convolution Networks for Multi-behavior Sequential Recommendation. Information Sciences, 720, Article 122484. https://doi.org/10.1016/j.ins.2025.122484 Wang, D., Guo, G., Ouyang, T., Yu, D., Zhang, H., Li, B., Jiang, R., Xu, G., & Deng, S. (2025). A Lightweight Spatio-Temporal Neural Network With Sampling-Based Time Series Decomposition for Traffic Forecasting. IEEE Transactions on Intelligent Transportation Systems, 26(6), 8682-8693. https://doi.org/10.1109/TITS.2025.3552010 Sun, X., Shi, K., Tang, H., Wang, D., Xu, G. & Li, Q. (2025). Educating Language Models as Promoters: Multi-aspect Instruction Alignment with Self-augmentation. IEEE Transactions on Knowledge and Data Engineering, 37(8), 4564-4577. https://doi.org/10.1109/TKDE.2025.3569585 Tang, H., Sun, X., Wu, S., Cui, Z., Xu, G., & Li, Q. (2025). DyBooster: Leveraging Large Language Model as Booster for Dynamic Recommendation. Expert Systems with Applications, 286, Article 128080. https://doi.org/10.1016/j.eswa.2025.128080 Tang, H., Wu, S., Cui, Z., Li, Y., Xu, G., & Li, Q. (2025). Model-Agnostic Dual-Side Online Fairness Learning for Dynamic Recommendation. IEEE Transactions on Knowledge and Data Engineering, 37(5), 2727-2742. https://doi.org/10.1109/TKDE.2025.3544510 Wang, D., Yao, H., Yu, D., Song, S., Weng, H., Xu, G., & Deng, S. (2025). Graph Intention Embedding Neural Network for Tag-aware Recommendation. Neural Networks, 184, Article 107062. https://doi.org/10.1016/j.neunet.2024.107062 Wang, Y., Xu, G., Cao, J., Chen, Y., & Wu, J. (2025). Does Digital Literacy Affect Farmers' Adoption of Agricultural Social Services? An Empirical Study Based on China Land Economic Survey Data. PLoS One, 20(4), Article e0320318. https://doi.org/10.1371/journal.pone.0320318 Yang, C., Chen, Y., Li, Z., Wang, X., Shi, K., Yao, L., Xu, G., & Guo, Z. (2025). Deep Multimodal Learning for Time Series Analysis in Social Computing: A Survey. International Journal of Multimedia Information Retrieval, 14(15), Article 15. https://doi.org/10.1007/s13735-025-00363-x Yu, D., Guo, G., Wang, D., Ouyang, T., Wan, F., Liu, J., Xu, G., & Deng, S. (2025). Dynamic Spatial-temporal Graph Convolution Network for e-Bike Traffic Flow Forecasting. IEEE Transactions on Vehicular Technology, 74(4), 5453-5466. https://doi.org/10.1109/TVT.2024.3508021 Li, Q., Wang, Z., Xia, H., Li, G., Cao, Y., Yao, L., & Xu, G. (2025). HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network. IEEE Transactions on Neural Networks and Learning Systems, 36(3), 4371-4384. https://doi.org/10.1109/TNNLS.2024.3370617 Lin, X., Liu, R., Cao, Y., Zou, L., Li, Q., Wu, Y., Liu, Y., Yin, D., & Xu, G. (2025). Contrastive Modality-Disentangled Learning for Multimodal Recommendation. ACM Transactions on Information Systems, 43(3), Article 70. https://doi.org/10.1145/3715876 Guo, S., Wang, C., Gao, C., Luo, W., Han, P., Liao, Q., & Xu, G. (2025). Improving Long-tail Classification via Decoupling and Regularisation. CAAI Transactions on Intelligence Technology, 10(1), 62-71. https://doi.org/10.1049/cit2.12374 Lai, W., Xie, H., Xu, G., & Li, Q. (2025). RVISA: Reasoning and Verification for Implicit Sentiment Analysis. IEEE Transactions on Affective Computing, Early Access, 1-12. https://doi.org/10.1109/TAFFC.2025.3537799 Li, A., Yang, B., Huo, H., Hussain, F. K., & Xu, G. (2025). Self-supervised Dual Graph Learning for Recommendation. Knowledge-Based Systems, 310, Article 112967. https://doi.org/10.1016/j.knosys.2025.112967 Yu, D., Li, Q., Wang, X., & Xu, G (2025). A Causal-based Attribute Selection Strategy for Conversational Recommender Systems. IEEE Transactions on Knowledge and Data Engineering, 37(5), 2169-2182. https://doi.org/10.1109/TKDE.2025.3543112 Cai, Q., Cao, J., Xu, G., & Zhu, N. (2025). Distributed Recommendation Systems: Survey and Research Directions. ACM Transactions on Information Systems, 43(1), Article 10. https://doi.org/10.1145/3694783 Tang, H., Wu, S., Sun, X., Zeng, J., Xu, G., & Li, Q. (2025). TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic Recommendation. ACM Transactions on Information Systems, 43(1), Article 5. https://doi.org/10.1145/3687470 Sui, S., Han, Q., Lu, D., Wu, S., & Xu, G. (2024). A Novel Complex Network Prediction Method Based on Multi-granularity Contrastive Learning. CCF Transactions on Pervasive Computing and Interaction, 6, 394-405. https://doi.org/10.1007/s42486-024-00174-9 Wu, Z., Liu, Y., Cen, J., Zheng, Z., & Xu, G. (2024). A Cross-domain Knowledge Tracing Model Based on Graph Optimal Transport. World Wide Web, 28, Article 10. https://doi.org/10.1007/s11280-024-01311-1 Yang, H., Wang, Y., Zhao, X., Chen, H., Yin, H., Li, Q., & Xu, G. (2024). Multi-level Graph Knowledge Contrastive Learning. IEEE Transactions on Knowledge and Data Engineering, 36(12), 8829-8841. https://doi.org/10.1109/TKDE.2024.3466530 Hanna, B., Xu, G., Wang, X., & Hossain, J. (2024). Integrating UN Sustainable Development Goals into Family Business Practices: A Perspective Article. Journal of Family Business Management, 14(6), 1203-1211. https://doi.org/10.1108/JFBM-10-2023-0243 Li, Z., Yang, C., Chen, Y., Wang, X., Chen, H., Xu, G., Yao, L., & Sheng, M. (2024). Graph and Sequential Neural Networks in Session-based Recommendation: A Survey. ACM Computing Surveys, 57(2), Article 40. https://doi.org/10.1145/3696413 Duong, T.D., Li, Q., & Xu, G. (2024). Causality-based Counterfactual Explanation for Classification Models. Knowledge-Based Systems, 300, Article 112200. https://doi.org/10.1016/j.knosys.2024.112200 Wang, X., Li, Q., Yu, D., Huang, W., Li, Q., & Xu, G. (2024). Neural Causal Graph Collaborative Filtering. Information Sciences, 677, Article 120872. https://doi.org/10.1016/j.ins.2024.120872 Wang, X., Li, Q., Yu, D., Li, Q., & Xu, G. (2024). Reinforced Path Reasoning for Counterfactual Explainable Recommendation. IEEE Transactions on Knowledge and Data Engineering, 36(7), 3443-3459. https://doi.org/10.1109/TKDE.2024.3354077 Yu, D., Wang, X., Xiong, Y., Shen, X., Wu, R., Wang, D., Zou, Z., & Xu, G. (2024). MHANER: A Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online Games. ACM Transactions on Intelligent Systems and Technology, 15(4), Article 85. https://doi.org/10.1145/3626243 Yicong Li; Xiangguo Sun; Hongxu Chen; Sixiao Zhang; Yu Yang; Guandong Xu (2024). Attention is not the only choice: Counterfactual reasoning for path-based explainable recommendation. IEEE Transactions on Knowledge and Data Engineering, 36(9), 4458-4471. https://doi.org/10.1109/TKDE.2024.3373608 SDGs infomation: 9 - Industry, Innovation and Infrastructure, 10 - Reduced Inequality Xiangmeng Wang, Qian Li, Dianer Yu, Qing Li, Guandong Xu (2024). Counterfactual explanation for fairness in recommendation. ACM Transactions on Information Systems, 42(4), Article 106. https://doi.org/10.1145/3643670 SDGs infomation: 5 - Gender Equality, 9 - Industry, Innovation and Infrastructure Islam, M. R., Akter, S., Islam, L., Razzak, I., Wang, X., & Xu, G. (2024). Strategies for Evaluating Visual Analytics Systems: A Systematic Review and New Perspectives. Information Visualization, 23(1), 84-101. https://doi.org/10.1177/14738716231212568
Conference Papers Sun, X., Shi, K., Tang, H., Xu, G., & Li, Q. (2025, June). Expert-guided Toxicity Filtration for Debiased Generation. [Paper presentation]. Advances in Knowledge Discovery and Data Mining: The 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia. https://doi.org/10.1007/978-981-96-8183-9_10 Bisht, N., Gong, X., & Xu, G. (2025, March). CUGF: A Reliable and Fair Recommendation Framework. [Paper presentation]. The 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, Pennsylvania, USA. https://doi.org/10.1609/aaai.v39i11.33245 Gong, X., Bisht, N., & Xu, G. (2025, March). Conformal Prediction for Partial Label Learning. [Paper presentation]. The 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, Pennsylvania, USA. https://doi.org/10.1609/aaai.v39i16.33853 Zhang, C., Feng, Z., Zhang, Z., Qiang, J., Xu, G., & Li, Y. (2025, March). Is LLMs Hallucination Usable? LLM-based Negative Reasoning for Fake News Detection. [Paper presentation]. The 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, Pennsylvania, USA. https://doi.org/10.1609/aaai.v39i1.32089 Shi, K., Sun, X., Wang, D., Fu, Y., Xu, G., & Li, Q. (2025, January). LLaMA-E: Empowering e-Commerce Authoring with Object-interleaved Instruction Following. [Poster presentation]. The 31st International Conference on Computational Linguistics, Abu Dhabi, UAE. https://coling2025.org/ Alsuhaibani, A., Razzak, I., Jameel, S., Wang, X., & Xu, G. (2024, December). CLIMB: Imbalanced Data Modelling Using Contrastive Learning with Limited Labels. [Paper presentation]. Web Information Systems Engineering: 25th International Conference, WISE 2024, Doha, Qatar. https://wise2024-qatar.com/ Liu, K., Zhao, F., Yang, Y., & Xu, G. (2024, October). DySarl: Dynamic Structure-Aware Representation Learning for Multimodal Knowledge Graph Reasoning. [Paper presentation]. The 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, Australia. https://doi.org/10.1145/3664647.3681020 Li, Y., Yang, Y., Cao, J., Liu, S., Tang, H., & Xu, G. (2024, August). Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach. [Paper presentation]. The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, Barcelona, Spain. https://doi.org/10.1145/3637528.3671848 Lu, D., Chen, X., Chen, R., Wu, S., & Xu, G. (2024, August). Fairness-aware Mutual Information for Multimodal Recommendation. [Paper presntation]. The 2024 IEEE International Conference on Behavioural and Social Computing (BESC-2024), Harbin, China. https://doi.org/10.1109/BESC64747.2024.10780480 Lu, D., Zhang, H., Li, L., Wu, S., & Xu, G. (2024, August). Cascading Hypergraph Convolution Networks for Multi-behavior Sequential Recommendation. [Paper presentation]. The 2024 IEEE International Conference on Behavioural and Social Computing (BESC-2024), Harbin, China. https://doi.org/10.1109/BESC64747.2024.10780584 Sui, S., Han, Q., Lu, D., Wu, S., & Xu, G. (2024, August). Enhancing Traffic Prediction via Spatial Multi-Granularity Co-Evolving Mechanism. [Paper presntation]. The 2024 IEEE International Conference on Behavioural and Social Computing (BESC-2024), Harbin, China. https://doi.org/10.1109/BESC64747.2024.10780632 Wu, S., & Xu, G. (2024, August). Learning Influential Relationships for Implicit Influence Maximization in Unknown Networks. [Paper presntation]. The 2024 IEEE International Conference on Behavioural and Social Computing (BESC-2024), Harbin, China. https://doi.org/10.1109/BESC64747.2024.10780571 Zhao, R., Zhao, F., Wang, L., Wang, X., & Xu, G. (2024, August). KG-CoT: Chain-of-thought Prompting of Large Language Models over Knowledge Graphs for Knowledge-aware Question Answering. [Paper presentation]. The Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24), Jeju, South Korea. https://ijcai24.org/index.html Deng, J., Shi, K., Huo, H., Wang, D., & Xu, G. (2024, July). Homogeneous-listing-augmented Self-supervised Multimodal Product Title Refinement. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington D.C., USA. https://doi.org/10.1145/3626772.3661347 Han, Q., Sui, S., Lu, D., Wu, S., Xu, G (2024, July). Enhancing Spatiotemporal Prediction with Intra- and Inter-granularity Contrastive Learning. [Paper presentation]. Database Systems for Advanced Applications: 29th International Conference, DASFAA 2024, Gifu, Japan. https://doi.org/10.1007/978-981-97-5779-4_13 Hu, K., Li, L., Xie, Q., Tao, X., & Xu, G. (2024, July). CrimeAlarm: Towards Intensive iIntent Dynamics in Fine-grained Crime Prediction. [Paper presentation]. Database Systems for Advanced Applications: The 29th International Conference, DASFAA 2024, Gifu, Japan. https://www.dasfaa2024.org/ Huang, S., Li, Q., Wang, X., Yu, D., Xu, G., & Li, Q. (2024, July). Counterfactual Debasing for Multi-behavior Recommendations. [Paper presentation]. Database systems for advanced applications: 29th International Conference, DASFAA 2024, Gifu, Japan. https://www.dasfaa2024.org/ Xiuwen Gong, Nitin Bisht, Guandong Xu (2024, July). Does label smoothing help deep partial label learning?. Proceedings of the 41st International Conference on Machine Learning http://www.scopus.com/inward/record.url?scp=85203788745&partnerID=8YFLogxK Scopus publicationLink to publication in Scopus, https://proceedings.mlr.press/v235/published version SDGs infomation: 4 - Quality Education, 9 - Industry, Innovation and Infrastructure
All Other Outputs Wan, S., Jin, Y., Xu, G., & Nappi, M. (2024). Editorial to Special Issue on Multimedia Cognitive Computing for Intelligent Transportation System. ACM Transactions on Multimedia Computing, Communications, and Applications. https://doi.org/10.1145/3604938

Projects


Enhancing Students’ Digital Competency by Developing Interactive and Immersive Educational Games in VRCAVE Project Start Year: 2025, Principal Investigator(s): XU, Guandong, FU, Hong
Generative Artificial Intelligence Empowered Collaborative Learning: Models, Algorithms, and System This project will research the comprehensive assessment metric of knowledge mastery in collaborative learning. We will devise novel algorithms to predict students’ learning performance in collaborative environment and a novel AIGC-based model to stimulate student learning and maximise the engagement as well as knowledge mastery for every student in the forum. In addition, we will develop an intelligent collaborative learning chatbot with a user-friendly interface to support front-line teaching. Project Start Year: 2024, Principal Investigator(s): XU, Guandong
AI in Contextual Teaching and Learning (CTL) and Self-directed Learning (SDL) for K-12 Students Project Start Year: 2024, Principal Investigator(s): XU, Guandong
Towards Demand-driven Education in AI Era: A Predictive Recommender Approach This project will research the impact of AI on job displacement and the evolving demand for job skills, aiming to develop novel algorithms to accurately predict the risk of job displacement and the upcoming trend of demanded job skills. An innovative demand-aware course recommendation system will be developed. Project Start Year: 2024, Principal Investigator(s): XU, Guandong