3 papers
cs.LG2025
LEGO: A Lightweight and Efficient Multiple-Attribute Unlearning Framework for Recommender Systems
Fengyuan Yu, Yuyuan Li, Xiaohua Feng +3
With the growing demand for safeguarding sensitive user information in recommender systems, recommendation attribute unlearning is receiving increasing attention. Existing studies…
cs.LG2025
Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols
Xiaohua Feng, Jiaming Zhang, Fengyuan Yu +7
With the rapid advancement of generative models, privacy, copyright, safety, and reliability risks have attracted growing attention. To mitigate these risks, machine unlearning has…
cs.IR2024
DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation
Heyuan Huang, Xingyu Lou, Chaochao Chen +5
Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an i…