Xiuqiang He

Xiuqiang He

Distinguished Professor at the College of Big data and Internet, Shenzhen Technology University (SZTU)

Xiuqiang He is now a Distinguished Professor at the College of Big Data and Internet, Shenzhen Technology University (SZTU). His research interests include recommendation systems, information retrieval, online marketing and advertisement, AI for science, and the application of artificial intelligence in real-world scenarios. Prior to his appointment at SZTU, he served as a Senior Director at FiT, Tencent from 2022 to 2024. Before that, he was the Director of the Recommendation and Search Lab at Huawei Noah’s Ark Laboratory. He earned his Ph.D. from the Department of Computer science and Engineering at the Hong Kong University of Science and Technology (HKUST) in 2010. He obtained his Master’s degree in 2006 and Bachelor’s degree in 2003, both from Xi’an JiaoTong University (XJTU). He has published over 100 research papers in top-tier international conferences and journals, and his publications have amassed over 9,000 citations on Google Scholar. He is among the authors of DeepFM, which is one of the most widely used click-through rate prediction algorithms in industry. He was honored with the Best Paper Award at DLP-KDD 2021. Additionally, he holds over 60 families of domestic or international patents.

📣 My group is looking for assistant professors, research assistants and visiting students. If you are interested in joining us and exploring exicting research opportunities, please don’t hesitate to reach out to me via email.

🔥 News

  • 2025.01:   I will serve on the program committee of IJCAI 2025 as a SPC Member.
  • 2025.01:   I will serve on the program committee of SIGIR 2025 as a PC Member.
  • 2025.01:   I will serve on the program committee of KDD 2025 ADS track as a PC member.
  • 2025.01:   I will serve on the program committee of ECMLPKDD 2025 as a PC member.

📝 Publications

Year 2025

  • Policy-aware Reward Modeling with Uncertainty-Gradient based Data Augmentation, Zexu Sun, Yiju Guo, Yankai Lin, Xu Chen, Qi Qi, Xing Tang , Xiuqiang He, and Ji-Rong Wen, The Thirteenth International Conference on Learning Representations, ICLR 2025
  • Scenario Shared Instance Modeling for Click-through Rate Prediction，Dugang Liu, Chaohua Yang, Yuwen Fu, Xing Tang, Gongfu Li, Fuyuan Lyu, Xiuqiang He, and Zhong Ming，Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025
  • Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models, Kexin Zhang, Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Kaize Ding, Xiuqiang He, and Xue Liu, Proceedings of the 18th ACM International Conference on Web Search and Data Mining, WSDM 2025
  • A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation, Xing Tang , Yunpeng Weng, Fuyuan Lyu, Dugang Liu, and Xiuqiang He, Database Systems for Advanced Applications - 30th International Conference, DASFAA 2025

Year 2024

  • OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction, Yunpeng Weng, Xing Tang, Zhenhao Xu, Fuyuan Lyu, Dugang Liu, Zexu Sun, and Xiuqiang He, Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024
  • End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling, Zexu Sun, Hao Yang, Dugang Liu, Yunpeng Weng, Xing Tang, and Xiuqiang He, Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024
  • Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation, Xing Tang, Yang Qiao, Fuyuan Lyu, Dugang Liu, and Xiuqiang He, Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024
  • Masked Random Noise for Communication-Efficient Federated Learning, Shiwei Li, Yingyi Cheng, Haozhao Wang, Xing Tang , Shijie Xu, Weihong Luo, Yuhua Li, Dugang Liu, Xiuqiang He, and Ruixuan Li Proceedings of the 31st ACM International Conference on Multimedia, MM 2024
  • Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing, Bowei He, Yunpeng Weng, Xing Tang , Ziqiang Cui, Zexu Sun, Liang Chen, Xiuqiang He, and Chen Ma, Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024
  • FedBAT: Communication-efficient Federated Learning via Learnable Binarization, Shiwei Li, Wenchao Xu, Haozhao Wang, Xing Tang, Yining Qi, Shijie Xu, Weihong Luo, Yuhua Li, Xiuqiang He, and Ruixuan Li, 41st International Conference on Machine Learning, ICML 2024
  • AutoDCS: Automated Decision Chain Selection in Deep Recommender Systems, Dugang Liu, Shenxian Xian, Yuhao Wu, Chaohua Yang, Xing Tang , Xiuqiang He, and Zhong Ming, Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024
  • Towards Effective and Efficient Multi-valued Treatment Uplift Modeling in Online Marketing, Zexun Sun, Dugang Liu, Xing Tang , Yunpeng Weng, and Xiuqiang He, Database Systems for Advanced Applications - 29th International Conference, DASFAA 2024
  • Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks, Ziqiang Cui, Xing Tang , Yang Qiao, Bowei He, Liang Chen, Xiuqiang He, and Chen Ma Proceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024
  • MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender Systems, Dugang Liu, Chaohua Yang, Xing Tang, Yejing Wang, Fuyuan Lyu, Weihong Luo, Xiuqiang He, Zhong Ming, and Xiangyu Zhao, Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024
  • Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects, Yuheng Cheng, Ceyao Zhang, Zhengwen Zhang, Xiangrui Meng, Sirui Hong, Wenhao Li, Zihao Wang, Zekai Wang, Feng Yin, Junhua Zhao, Xiuqiang He, arXiv preprint arXiv:2401.03428

Year 2023

  • Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network, Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Weihong Luo, Liang Chen, Xiuqiang He, and Xue Liu, 37th Annual Conference on Neural Information Processing Systems, NeurIPS 2023
  • Prior-guided Accuracy-bias Tradeoff Learning for CTR Prediction in Multimedia Recommendation, Dugang Liu, Yang Qiao, Xing Tang , Liang Chen, Xiuqiang He, and Zhong Ming, Proceedings of The 30th ACM International Conference on Multimedia, MM 2023
  • OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction, Xing Tang, Yang Qiao, Yuwen Fu, Fuyuan Lyu, Dugang Liu, and Xiuqiang He, European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2023
  • Expected Transaction Value Optimization for Precise Marketing in FinTech Platforms, Yunpeng Weng, Xing Tang , Liang Chen, Dugang Liu, and Xiuqiang He, International Workshop on Deep Learning Practice for High-Dimensional Sparse Data, DLP@Recsys 2023
  • Explicit Feature Interaction-aware Uplift Network for Online Marketing, Dugang Liu, Xing Tang, Gao Han, Fuyuan Lyu, and Xiuqiang He, Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023
  • Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing, Yunpeng Weng, Xing Tang, Liang Chen, and Xiuqiang He, Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023
  • DIWIFT: Discovering Instance-wise Influential Features for Tabular Data, Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang, Yanyu Chen, Xiaoting Wang, Weike Pan, Zhong Ming, and Xiuqiang He, Proceedings of the ACM Web Conference 2023, WebConf 2023
  • Optimizing Feature Set for Click-Through Rate Prediction, Fuyuan Lyu, Xing Tang , Dugang Liu, Liang Chen, Xiuqiang He, and Xue Liu, Proceedings of the ACM Web Conference 2023, WebConf 2023
  • Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation, Dugang Liu, Qiao Yang, Xing Tang , Liang Chen, Xiuqiang He, Weike Pan, and Zhong Ming Database Systems for Advanced Applications - 28th International Conference, DASFAA 2023
  • Debiased recommendation with user feature balancing, Mengyue Yang, Guohao Cai, Furui Liu, Jiarui Jin, Zhenhua Dong, Xiuqiang He, Jianye Hao, Weiqi Shao, Jun Wang, Xu Chen, ACM Transactions on Information Systems, TOIS

👪 Group

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🧑‍💻 Services

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📖 Educations

  • 2006.07 - 2010.07, Ph.D, Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST)
  • 2003.09 - 2006.07, Master, Computer Science and Technology, Xi’an Jiaotong University
  • 1999.09 - 2003.07, Bachelor, Computer Science and Technology, Xi’an Jiaotong University

💻 Industry Experiences

  • 2022.01 - 2025.01, Senior Director, Principle Expert Researcher, Intelligent Application Center, FiT, Tencent
  • 2019.07 - 2021.12, Director, Senior Research, Recommendation and search Lab, Noah’s Ark Lab, Huawei
  • 2018.05 - 2019.07, Algorithm Expert, PCG, Tencent
  • 2010.07 - 2018.05, Founding Director, Recommendation and search Lab, Noah’s Ark Lab, Huawei