Wu, Chuhan

How Can Recommender Systems Benefit from Large Language Models: A Survey

Lin, J., Dai, X., Xi, Y., Liu, W., Chen, B., Zhang, H., Liu, Y., Wu, C., Li, X., Zhu, C., et al. (2025) · ACM Transactions on Information Systems

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 242

Personalized News Recommendation: Methods and Challenges

Wu, C., Wu, F., Huang, Y., & Xie, X. (2023) · ACM Transactions on Information Systems

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 240

Differentially Private Knowledge Transfer for Federated Learning

Qi, T., Wu, F., Wu, C., Huang, Y., & Xie, X. (2022) · Nature Communications

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 65

Differentially private knowledge transfer for federated learning

Qi, T., Wu, F., Wu, C., He, L., Huang, Y., & Xie, X. (2023) · Nature Communications

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 65

ModelShield: Adaptive and Robust Watermark Against Model Extraction Attack

Pang, K., Qi, T., Wu, C., Bai, M., Jiang, M., & Huang, Y. (2025) · IEEE Transactions on Information Forensics and Security

Adversarial Robustness in Machine LearningAdvanced Steganography and Watermarking Techniques

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Learning to edit: Aligning llms with knowledge editing

Jiang, Y., Wang, Y., Wu, C., Zhong, W., Zeng, X., Gao, J., Li, L., Jiang, X., Shang, L., Tang, R., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 12