Xing Tang

Senior Researcher, Tencent
xing.tang [at] hotmail.com, shawntang [at] tencent.com
Google scholar | DBLP | OpenReview | Github

Dr. Xing Tang is currently a Senior Researcher at Tencent. He obtained his Ph.D. from Computer Science, Xidian University in 2016, under the supervision of Prof. Qiguang Miao. After graduation, He joined Huawei Noah’s Ark Lab, and worked with Dr.Xiuqiang He and Dr.Ruiming Tang. He also worked in Tencent ARC Lab with Dr.Ying Shan for a while. Now he works for FiT, Tencent and focus on large language model and recommendation. His research interests mainly focus on building recommender systems and uplift modeling for financial products, finanical large language model.

Research interest: Recommendation, Uplift Modeling, Large Language Model.

Announcement: Interested in internship or collaboration. You can contact me if you are interested!

News

Apr 5, 2025 Two papers about LLM for recommendation and Multi-scenario recommendation is accepted by SIGIR 2025.
Mar 19, 2025 I will serve as PC member for RecSys 2025 and CIKM 2025.
Mar 13, 2025 I will serve as PC member for ACML 2025 and IEEE BigData 2025.
Mar 5, 2025 One paper about financial time series forcasting is accepted by Financial AI@ICLR 2025.
Feb 18, 2025 I will serve as PC member for Neurips 2025.

Publications( * represents co-first author, # represents corresponding author. )

Journal & Conference
  1. Comprehending Knowledge Graphs with Large Language Models for Recommender Systems
    Ziqiang Cui, Yunpeng Weng, Xing Tang# , Fuyuan Lyu, Dugang Liu, Xiuqiang He, and Chen Ma#
    Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025 | [ ]
  2. Multi-scenario Instance Embedding Learning for Deep Recommneder Systems
    Chaohua Yang, Dugang Liu, Xing Tang , Yuwen Fu, Xiuqiang He, Xiangyu Zhao, and Ming Zhong
    Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025 | [ ]
  3. Timing is important: Risk-aware Fund Allocation based on Time-Series Forcasting
    Fuyuan Lyu, Linfeng Du, Yunpeng Weng, Qiufang Ying, Zhiyan Xu, Wen Zou, Haolun Wu, Xiuqiang He, and Xing Tang#
    The Thirteenth International Conference on Learning Representations Workshop Advances in Financial AI, Financial AI@ICLR 2025 | [ HTML ]
  4. 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 | [ ]
  5. 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 | [ HTML ]
  6. 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 | [ ]
  7. 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 | [ HTML PDF ]
  8. 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 | [ HTML PDF ]
  9. 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 | [ HTML PDF ]
  10. 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 | [ HTML PDF ]
  11. 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 | [ HTML ]
  12. 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 | [ HTML PDF ]
  13. 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 | [ HTML PDF ]
  14. 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 | [ HTML PDF ]
  15. 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 | [ HTML PDF ]
  16. RecDCL: Dual Contrastive Learning for Recommendation
    Dan Zhang, Yangliao Geng, Wenwen Gong, Zhongang Qi, Zhiyu Chen, Xing Tang , Ying Shan, Yuxiao Dong, and Jie Tang
    Proceedings of the ACM Web Conference 2024,WebConf 2024 | [ HTML PDF Code ]
  17. 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 | [ HTML PDF ]
  18. Embedding Compression in Recommender Systems:A Survey
    Shiwei Li, Huifeng Guo, Xing Tang , Ruiming Tang, Lu Hou, Ruixuan Li, and Rui Zhang
    ACM Computing Surveys | [ HTML PDF ]
  19. 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 | [ HTML PDF ]
  20. 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 | [ HTML PDF Code ]
  21. 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 | [ HTML PDF ]
  22. 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 | [ HTML PDF ]
  23. 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 | [ HTML ]
  24. AutoOpt: Automatic Hyperparameter Scheduling and Optimization for Deep Click-through Rate Prediction
    Yujun Li*, Xing Tang* , Bo Chen*, Yimin Huang, Ruiming Tang, and Zhenguo Li
    Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023 | [ HTML PDF ]
  25. 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 | [ HTML PDF Code ]
  26. 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 | [ HTML PDF ]
  27. Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction
    Shiwei Li, Huifeng Guo, Lu Hou, Wei Zhang, Xing Tang , Ruiming Tang, Rui Zhang, and Ruixuan Li
    Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023 | [ HTML PDF ]
  28. 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 | [ HTML PDF Code ]
  29. 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 | [ HTML PDF Code ]
  30. 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 | [ HTML PDF ]
  31. OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
    Fuyuan Lyu*, Xing Tang* , Hong Zhu, Huifeng Guo, Yingxue Zhang, Ruiming Tang, and Xue Liu
    Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM 2022 | [ HTML PDF Code ]
  32. Memorize, Factorize, or be Naive: Learning Optimal Feature Interaction Methods for CTR Prediction
    Fuyuan Lyu*, Xing Tang* , Huifeng Guo, Ruiming Tang, Xiuqiang He, Rui Zhang, and Xue Liu
    38th IEEE International Conference on Data Engineering, ICDE 2022 | [ HTML PDF Code ]
  33. Mobile App Cross-Domain Recommendation with Multi-Graph Neural Network
    Yi Ouyang*, Bin Guo, Xing Tang* , Xiuqiang He, Jian Xiong, and Zhiwen Yu
    ACM Transaction on Knowledge Discovery from Data 2021 | [ HTML PDF ]
  34. AutoConjunction: Adaptive Model-based Feature Conjunction for CTR Prediction
    Chih-Yao Chang*, Xing Tang* , Bo-Wen Yuan, Jui-Yang Hsia, Zhirong Liu, Zhenhua Dong, Xiuqiang He, and Chih-Jen Lin
    21st IEEE International Conference on Mobile Data Management, MDM 2020 | [ HTML ]
  35. A Data-Based Approach to Discovering Multi-Topic Influential Leaders
    Xing Tang , Qiguang Miao, Shangshang Yu, and Yining Quan
    PLOS ONE 2016 | [ HTML ]
  36. Predicting individual retweet behavior by user similarity: A multi-task learning approach
    Xing Tang , Qiguang Miao, Yi-Ning Quan, Jie Tang, and Kai Deng
    Knowledge-Based System 2015 | [ HTML ]
  37. A Novel Email Virus Propagation Model with Local Group
    Qiguang Miao, Xing Tang , and Yi-Ning Quan
    IEEE 11th Intl Conf on Ubiquitous Intelligence and Computing, UIC 2014 | [ HTML ]
Preprint
  1. Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
    Ziqiang Cui, Yunpeng Weng, Xing Tang , Xiaokun Zhang, Dugang Liu, Shiwei Li, Peiyang Liu, Bowei He, Weihong Luo, Xiuqiang He, and Chen Ma
    arXiv preprint arXiv:2503.04162 2025 | [ arXiv ]
  2. Mixed-Precision Embeddings for Large-Scale Recommendation Models
    Shiwei Li, Zhuoqi Hu, Fuyuan Lyu, Xing Tang , Haozhao Wang, Shijie Xu, Weihong Luo, Yuhua Li, Xue Liu, Xiuqiang He, and Ruixuan Li
    arXiv preprint arXiv:2409.20305 2024 | [ arXiv ]
  3. Benchmarking for Deep Uplift Modeling in Online Marketing
    Dugang Liu, Xing Tang , Yang Qiao, Miao Liu, Zexu Sun, Xiuqiang He, and Ming Zhong
    arXiv preprint arXiv:2406.00335 2024 | [ arXiv ]
  4. Towards Automated Negative Sampling in Implicit Recommendation
    Fuyuan Lyu, Yaochen Hu, Xing Tang , Yingxue Zhang, Ruiming Tang, and Xue Liu
    arXiv preprint arXiv.2311.03526 2023 | [ arXiv ]
  5. Feature Representation Learning for Click-through Rate Prediction:A Review and New Perspectives
    Fuyuan Lyu, Xing Tang , Dugang Liu, Haolun Wu, Chen Ma, Xiuqiang He, and Xue Liu
    arXiv preprint arXiv.2302.02241 2023 | [ arXiv ]
  6. Robust Long-Tailed Learning via Label-Aware Bounded CVaR
    Hong Zhu, Runpeng Yu, Xing Tang , Yifei Wang, Yuan Fang, and Yisen Wang
    arXiv preprint arXiv.2308.15405 2023 | [ arXiv PDF ]

Services

Academic service
  • Conference PC&AC member
    • AI area: AAAI(2024-2025); IJCAI(2024-2025); AISTATS(2025); IJCNN(2025)
    • ML area: ICML(2024-2025); Neurips(2024-2025); ICLR(2025); ACML(2024-2025)
    • DM area: KDD(2019-2025); WSDM(2024-2025); CIKM(2023-2025); ECML-PKDD(2024-2025); IEEE BigData(2024-2025)
    • IR area: WebConf(2024-2025); Recsys(2024-2025); SIGIR(2025)
    • Others: MM(2025)
  • Journal reviewer
    • IEEE Transactions on Knowledge and Data Engineering(TKDE)
    • Pattern Recognition(PR)
    • Knowledge-Based Systems(KBS)
    • Engineering Applications of Artificial Intelligence(EAAI)
    • Expert Systems With Applications(ESWA)