cf Dugang Liu - Shenzhen University
Dugang Liu

Dugang Liu

(刘杜钢)

Hello! I am an Associate Researcher in the Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) at Shenzhen University. I got my Ph.D. from the College of Computer Science and Software Engineering at Shenzhen University, advised by Prof. Zhong Ming and Weike Pan. Before that, I obtained my Master's degree in Computer Science from Xiamen University and my Bachelor's degree in Mathematics from Fujian Normal University.

I am broadly interested in data mining, data-centric artificial intelligence, and user-centric personalized services, with a special emphasis on enhancing the responsibility in algorithms to provide fair, trustworthy, effective and efficient user-centric information services for various application scenarios. I am open to various kinds of collaborations. If you have any ideas, please contact me.

I am looking for multiple self-motivated Masters and PhD students. If you are interested, please contact me.

For Prospective Students

For prospective Masters and PhD students: I am looking for highly self-motivated Masters and PhD students. If you are interested in working with me, please email me with your CV, transcript, a description of your research interests and experience, and your writting samples (e.g., publications, dissertations, project reports, and so on).

For prospective intern students: I am also exicted to help other students with their research, especially if you plan to apply for higher degrees at graduate schools. Full-time interns will be offered a salary accordingly. If you are interested in working with me, please email me with your CV, transcript, a description of your research interests and experience, and your writting samples (e.g., publications, dissertations, project reports, and so on).

News

  • 11/2024: Two papers about automated multi-scenario instance selection and real-time uplift modeling are accepted by KDD 2025, congratulations to my student Chaohua and all collaborators!
  • 10/2024: One paper about fusion design of click-through rate prediction model is accepted to WSDM 2025, congratulations to all collaborators!
  • 10/2024: I received the RecSys 2024 Outstanding Reviewer Award.
  • 09/2024: One paper about LLM-enhanced recommendations is accepted by EARL@RecSys 2024, congratulations to my student Shenxian!
  • 09/2024: Invited to be a reviewer for AISTATS 2025.
  • 08/2024: One paper about context-aware recommendation is accepted by ACM TORS, congratulations to my students Shenxian and Yuhao!
  • 08/2024: Invited to be a reviewer for WWW 2025 and ICLR 2025.
  • 07/2024: Two papers about multi-task recommendation with hybrid targets and incentive recommendation are accepted to RecSys 2024, congratulations to all collaborators!
  • 07/2024: One paper about lifetime value prediction is accepted to CIKM 2024, congratulations to all collaborators!
  • 07/2024: One paper about efficiency optimization of federated learning is accepted to MM 2024, congratulations to all collaborators!
  • 07/2024: Invited to be a reviewer for KDD 2025.
  • 06/2024: Invited to be a PC member for AAAI 2025, ACML 2024 and WSDM 2025.
  • 05/2024: Invited to be a reviewer for NeurIPS 2024.
  • 03/2024: One paper about automated user behavior selection is accepted by SIGIR 2024, congratulations to my students Shenxian, Yuhao and Chaohua!
  • 03/2024: Invited to be a PC member for CIKM 2024 and ECMLPKDD 2024.
  • 03/2024: One paper about multi-valued treatment uplift modeling is accepted by DASFAA 2024, congratulations to Zexu!
  • 03/2024: One paper about automated bug detection with LLM is accepted to TKDD, congratulations to all collaborators!
  • 02/2024: Invited to be a PC member for RecSys 2024, BigData 2024 and ICML 2024.
  • 02/2024: One paper about generative recommendation is accepted to LREC-COLING 2024, congratulations to all collaborators!
  • 01/2024: Invited to be a PC member for IJCAI 2024, KDD 2024, MM 2024, and ACL 2024.
  • 10/2023: One paper about automated multi-scenario feature selection is accepted by WSDM 2024, congratulations to my student Chaohua!
  • 09/2023: One paper about automated feature interaction selection is accepted by NeurIPS 2023, congratulations to all collaborators!
  • 09/2023: Our work received RecSys 2023 Best Full Paper Runner-up Award, congratulations to all collaborators!
  • 09/2023: Invited to be a PC member for AISTATS 2024, WWW 2024, and SDM 2024.
  • 09/2023: One paper about uplift modeling is accepted by ICDM 2023, congratulations to Zexu!
  • 08/2023: Received funding from the Youth Project of the National Natural Science Foundation of China.
  • 08/2023: One paper about debiasing recommendation is accepted by Artificial Intelligence, congratulations to Zinan!
  • 07/2023: One paper about debiasing recommendation is accepted by MM 2023, congratulations to all collaborators!
  • 06/2023: One paper about context-aware recommendation is accepted by RecSys 2023, congratulations to all collaborators!
  • 06/2023: One paper about multi-scenario recommendation is accepted by ECMLPKDD 2023, congratulations to all collaborators!
  • 05/2023: One paper about uplift modeling is accepted by KDD 2023, congratulations to all collaborators!

Students

  • Miao Liu, MS, 09/2022 -
  • Tingting Song, MS, 09/2022 -
  • Shenxian Xian, MS, 09/2022 -
  • Chaohua Yang, MS, 09/2022 -
  • Yuhao Wu, PhD, 09/2023 -
  • Chao Jiang, MS, 09/2024 -
  • Shuying Jiang, MS, 09/2024 -
  • Guorui Li, MS, 09/2024 -
  • Ruilin Yuan, MS, 09/2024 -
  • Jun Zhang, MS, 09/2024 -
  • Kailiang Hao, MS, 09/2024 -

    • Publications

    (Google Scholar) (Semantic Scholar)
    • [KDD 2025] Robust uplift modeling with large-scale contexts for real-time marketing. (acceptance rate: 19%)
      The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Research), 2025.
      Zexu Sun, Qiyu Han, Minqin Zhu, Hao Gong, Dugang Liu*, Chen Ma*.
    • [KDD 2025] Scenario shared instance modeling for click-through rate prediction. (acceptance rate: 22%)
      The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (ADS), 2025.
      Dugang Liu, Chaohua Yang, Yuwen Fu, Xing Tang, Gongfu Li, Fuyuan Lyu, Xiuqiang He, Zhong Ming*.
    • [WSDM 2025] Fusion matters: Learning fusion in deep click-through rate prediction models. (acceptance rate: 17.3%)
      The 18th ACM International Conference on Web Search and Data Mining, 2025.
      Kexin Zhang, Fuyuan Lyu, Xing Tang*, Dugang Liu*, Chen Ma, Kaize Ding, Xiuqiang He, Xue Liu.
    • [EARL@RecSys 2024] A Practice-friendly LLM-enhanced paradigm with preference parsing for sequential recommendation. [arxiv version]
      The 1th Workshop on Evaluating and Applying Recommendation Systems with Large Language Models at RecSys, 2024.
      Dugang Liu, Shenxian Xian, Xiaolin Lin, Xiaolian Zhang, Hong Zhu, Yuan Fang, Zhen Chen, Zhong Ming.
    • [TORS 2024] Pairwise intent graph embedding learning for context-aware recommendation with knowledge graph. [pdf] [code]
      ACM Transactions on Recommender Systems, 2024.
      Dugang Liu, Shenxian Xian, Yuhao Wu, Xiaolian Zhang, Zhong Ming*.
    • [RecSys 2024] End-to-end cost-effective incentive recommendation under budget constraint with uplift modeling. [pdf] [arxiv version]
      The 18th ACM Conference on Recommender Systems, 2024.
      Zexu Sun, Hao Yang, Dugang Liu*, Yunpeng Weng, Xing Tang*, Xiuqiang He.
    • [RecSys 2024] Touch the core: Exploring task dependence among hybrid targets for recommendation. [pdf] [code] [arxiv version]
      The 18th ACM Conference on Recommender Systems, 2024.
      Xing Tang, Yang Qiao, Fuyuan Lyu, Dugang Liu*, Xiuqiang He*.
    • [CIKM 2024] OptDist: Learning optimal distribution for customer lifetime value prediction. [pdf] [code] [arxiv version]
      The 33rd ACM International Conference on Information and Knowledge Management, 2024.
      Yunpeng Weng, Xing Tang, Zhenhao Xu, Fuyuan Lyu, Dugang Liu*, Zexu Sun, Xiuqiang He*.
    • [MM 2024] Masked random noise for communication-efficient federated learning. [pdf] [code] [arxiv version]
      The 32nd ACM International Conference on Multimedia, 2024.
      Shiwei Li, Yingyi Cheng, Haozhao Wang*, Xing Tang, Shijie Xu, Weihong Luo, Yuhua Li, Dugang Liu, Xiuqiang He, Ruixuan Li.
    • [SIGIR 2024] AutoDCS: Automated decision chain selection in deep recommender systems. [pdf] [code]
      The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024.
      Dugang Liu, Shenxian Xian, Yuhao Wu, Chaohua Yang, Xing Tang, Xiuqiang He, Zhong Ming*.
    • [DASFAA 2024] Towards effective and efficient multi-valued treatment uplift modeling in online marketing. [pdf]
      The 29th International Conference on Database Systems for Advanced Applications, 2024.
      Zexu Sun, Dugang Liu*, Xing Tang, Yunpeng Weng, Xiuqiang He.
    • [TKDD 2024] Automatically inspecting thousands of static bug warnings with large language model: How far are we?. [pdf] [code]
      ACM Transactions on Knowledge Discovery from Data, 2024.
      Cheng Wen, Yuandao Cai, Bin Zhang, Jie Su*, Zhuwu Xu, Dugang Liu, Shengchao Qin*, Zhong Ming, Cong Tian.
    • [LREC-COLING 2024] Large language models for generative recommendation: A survey and visionary discussions. [pdf] [arxiv version]
      The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, 2024.
      Lei Li, Yongfeng Zhang, Dugang Liu, Li Chen.
    • [WSDM 2024] MultiFS: Automated multi-scenario feature selection in deep recommender systems. [pdf] [code]
      The 17th ACM International Conference on Web Search and Data Mining, 2024.
      Dugang Liu, Chaohua Yang, Xing Tang, Yejing Wang, Fuyuan Lyu, Weihong Luo, Xiuqiang He, Zhong Ming*, Xiangyu Zhao*.
    • [NeurIPS 2023] Towards hybrid-grained feature interaction selection for deep sparse network. [pdf] [code] [arxiv version]
      The 37th Conference on Neural Information Processing Systems, 2023.
      Fuyuan Lyu, Xing Tang*, Dugang Liu, Chen Ma, Weihong Luo, Liang Chen, Xiuqiang He, Xue Liu.
    • [ICDM 2023] Robustness-enhanced uplift modeling with adversarial feature desensitization. [pdf] [arxiv version]
      The 23rd IEEE International Conference on Data Mining, 2023.
      Zexu Sun, Bowei He, Ming Ma, Jiakai Tang, Yuchen Wang, Chen Ma, Dugang Liu*.
    • [MM 2023] Prior-guided accuracy-bias tradeoff learning for CTR prediction in multimedia recommendation. [pdf]
      The 31st ACM International Conference on Multimedia, 2023.
      Dugang Liu, Yang Qiao, Xing Tang, Liang Chen, Xiuqiang He, Zhong Ming*.
    • [RecSys 2023] Pairwise intent graph embedding learning for context-aware recommendation. [Best Full Paper Runner-up Award] [pdf] [code]
      The 17th ACM Conference on Recommender Systems, 2023.
      Dugang Liu, Yuhao Wu, Weixin Li, Xiaolian Zhang, Hao Wang, Qinjuan Yang, Zhong Ming*.
    • [DLP@RecSys 2023] Expected transaction value optimization for precise marketing in FinTech platforms. [arxiv version]
      The 5th Workshop on Deep Learning Practice for High-Dimensional Sparse Data at RecSys, 2023.
      Yunpeng Weng, Xing Tang, Liang Chen, Dugang Liu, Xiuqiang He.
    • [ECMLPKDD 2023] OptMSM: Optimizing multi-scenario modeling for click-through rate prediction. [pdf] [arxiv version]
      The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2023: ADS and Demo Track, 2023.
      Xing Tang, Yang Qiao, Yuwen Fu, Fuyuan Lyu, Dugang Liu*, Xiuqiang He*.
    • [AIJ 2023] Transfer learning for collaborative recommendation with biased and unbiased data. [pdf] [code]
      Artificial Intelligence, 2023.
      Zinan Lin, Dugang Liu, Weike Pan*, Qiang Yang, Zhong Ming.
    • [KDD 2023] Explicit feature interaction-aware uplift network for online marketing. [pdf] [code] [arxiv version]
      The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023.
      Dugang Liu, Xing Tang*, Han Gao, Fuyuan Lyu, Xiuqiang He*.
    • [TKDE 2023] KDCRec: Knowledge distillation for counterfactual recommendation via uniform data. [pdf] [code]
      IEEE Transactions on Knowledge and Data Engineering, 2023.
      Dugang Liu, Pengxiang Cheng, Zinan Lin, Jinwei Luo, Zhenhua Dong, Xiuqiang He, Weike Pan*, Zhong Ming*.
    • [WWW 2023] Optimizing feature set for click-through rate prediction. [pdf] [code] [arxiv version]
      The ACM Web Conference 2023, 2023.
      Fuyuan Lyu, Xing Tang, Dugang Liu*, Liang Chen, Xiuqiang He*, Xue Liu.
    • [WWW 2023] DIWIFT: Discovering instance-wise influential features for tabular data. [pdf] [code] [arxiv version]
      The ACM Web Conference 2023, 2023.
      Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang, Yanyu Chen, Xiaoting Wang, Weike Pan*, Zhong Ming*.
    • [TOIS 2023] Bounding system-induced biases in recommender systems with a randomized dataset. [pdf] [arxiv version]
      ACM Transactions on Information Systems, 2023.
      Dugang Liu, Pengxiang Cheng, Zinan Lin, Xiaolian Zhang, Zhenhua Dong, Rui Zhang, Xiuqiang He, Weike Pan*, Zhong Ming*.
    • [DASFAA 2023] Self-sampling training and evaluation for the accuracy-bias tradeoff in recommendation. [pdf] [arxiv version]
      The 28th International Conference on Database Systems for Advanced Applications, 2023.
      Dugang Liu, Yang Qiao, Xing Tang, Liang Chen, Xiuqiang He, Weike Pan*, Zhong Ming*.
    • [TORS 2023] Debiased representation learning in recommendation via information bottleneck. [pdf] [code]
      ACM Transactions on Recommender Systems, 2023.
      Dugang Liu, Pengxiang Cheng, Hong Zhu, Zhenhua Dong, Xiuqiang He, Weike Pan*, Zhong Ming*.
    • [COLING 2022] Augmenting legal judgment prediction with contrastive case relations. [pdf] [code]
      The 29th International Conference on Computational Linguistics, 2022.
      Dugang Liu, Weihao Du, Lei Li, Weike Pan*, Zhong Ming*.
    • [DSAA 2022] ALTRec: Adversarial learning for autoencoder-based tail recommendation. [pdf] [code]
      The 9th IEEE International Conference on Data Science and Advanced Analytics, 2022.
      Jixiong Liu, Dugang Liu, Weike Pan*, Zhong Ming*.
    • [KDD 2022] User-event graph embedding learning for context-aware recommendation. [pdf] [code]
      The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022.
      Dugang Liu, Mingkai He, Jinwei Luo, Jiangxu Lin, Meng Wang, Xiaolian Zhang, Weike Pan*, Zhong Ming*.
    • [IJCNN 2022] SQL-Rank++: A novel listwise approach for collaborative ranking with implicit feedback. [pdf] [code]
      The 2022 International Joint Conference on Neural Networks, 2022.
      Zheng Yuan, Dugang Liu, Weike Pan*, Zhong Ming*.
    • [TKDE 2022] Spiral of silence and its application in recommender systems. [pdf] [code]
      IEEE Transactions on Knowledge and Data Engineering, 2022.
      Chen Lin*, Dugang Liu, Hanghang Tong, Yanghua Xiao.
    • [RecSys 2021] Mitigating confounding bias in recommendation via information bottleneck. [pdf] [code]
      The 15th ACM Conference on Recommender Systems, 2021.
      Dugang Liu, Pengxiang Cheng, Hong Zhu, Zhenhua Dong, Xiuqiang He, Weike Pan*, Zhong Ming*.
    • [RecSys 2021] Transfer learning in collaborative recommendation for bias reduction. (LBR paper) [pdf] [code]
      The 15th ACM Conference on Recommender Systems, 2021.
      Zinan Lin, Dugang Liu, Weike Pan*, Zhong Ming*.
    • [JCS 2021] Tri-task variational autoencoder for modeling of biased and unbiased unary feedback in recommender systems. [pdf] [code]
      Journal of Cyber Security, 2021.
      Zinan Lin, Dugang Liu, Weike Pan*, Zhong Ming.
    • [CCML 2021] Unbiased recommendation model based on improved propensity score estimation. [pdf] [code]
      The 18th China Conference on Machine Learning, 2021.
      Jinwei Luo, Dugang Liu, Weike Pan*, Zhong Ming.
    • [DLP@KDD 2020] FLEN: Leveraging field for scalable CTR prediction. [arxiv version] [code]
      The 2nd Workshop on Deep Learning Practice for High-Dimensional Sparse Data at KDD, 2020.
      Wenqiang Chen, Lizhang Zhan, Yuanlong Ci, Minghua Yang, Chen Lin*, Dugang Liu*.
    • [SIGIR 2020] A general knowledge distillation framework for counterfactual recommendation via uniform data. [pdf] [code]
      The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
      Dugang Liu, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Weike Pan*, Zhong Ming*.
    • [WSDM 2019] Spiral of silence in recommender systems. [pdf] [code]
      The 12th ACM International Conference on Web Search and Data Mining, 2019.
      Dugang Liu, Chen Lin*, Zhilin Zhang, Yanghua Xiao, Hanghang Tong.

    Academic Service

    • Conference program committees: KDD (2023/2024/2025), WWW (2024/2025), RecSys (2023/2024), WSDM (2024/2025), CIKM (2023/2024), SDM (2024), ECMLPKDD (2024), BigData (2024), AAAI (2021/2024/2025), IJCAI (2023/2024), ICLR (2025), ICML (2024), NeurIPS (2024), AISTATS (2024/2025), ACML (2024), ACL (2024), MM (2023/2024), ICONIP (2021/2022), SAC (2021/2022)
    • Journal reviewers: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Information Forensics and Security (T-IFS), IEEE Transactions on Software Engineering and Methodology (TOSEM), IEEE Transactions on Services Computing (TSC), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), IEEE Transactions on Knowledge Discovery from Data (TKDD), IEEE Transactions on Big Data (TBD), ACM Transactions on Information Systems (TOIS), ACM Transactions on Recommender Systems (TORS), Computers & Security (), Information Sciences (INS), Knowledge-Based Systems (KBS), Expert Systems With Applications (ESWA), Swarm and Evolutionary Computation (SWEVO), Journal of Computer Science and Technology (JCST), Journal of Artificial Intelligence Research (JAIR), Applied Soft Computing (ASOC), Journal of Intelligent Information Systems (JIIS)

    Awards & Honors

    • RecSys'24 Outstanding Reviewer, 2024
    • RecSys'23 Best Full Paper Runner-up Award, 2023
    • SZU Excellent Doctoral Thesis Award (Top 1), 2023

    Acknowledgments: webpage template borrows from Dr. Ziwei Zhu. Last update: November 20, 2024.