26 citations · 51 across the 17 of their papers we have counts for
9 papers · 1 filter
BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimization
Jiaming Zhang, Yuyuan Li, Yiqun Xu +4
Large Language Model-enhanced Recommender Systems (LLM-enhanced RSs) have emerged as a powerful approach to improving recommendation quality by leveraging LLMs to generate item rep…
Reproducibility Companion Paper:In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
Yixiu Liu, Zehui He, Yuyuan Li +3
In this paper, we reproduce experimental results presented in our earlier work titled "In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender System…
Reproducibility Companion Paper: Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
Yuyuan Li, Junjie Fang, Chaochao Chen +3
In this paper, we reproduce the experimental results presented in our previous work titled "Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems," which…
A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions
Yuyuan Li, Xiaohua Feng, Chaochao Chen +1
Recommender systems have become increasingly influential in shaping user behavior and decision-making, highlighting their growing impact in various domains. Meanwhile, the widespre…
CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
Chaochao Chen, Jiaming Zhang, Yizhao Zhang +5
With increasing privacy concerns in artificial intelligence, regulations have mandated the right to be forgotten, granting individuals the right to withdraw their data from models.…
Post-Training Attribute Unlearning in Recommender Systems
Chaochao Chen, Yizhao Zhang, Yuyuan Li +5
With the growing privacy concerns in recommender systems, recommendation unlearning is getting increasing attention. Existing studies predominantly use training data, i.e., model i…
In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
Zhongxuan Han, Chaochao Chen, Xiaolin Zheng +4
Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The ex…
Selective and Collaborative Influence Function for Efficient Recommendation Unlearning
Yuyuan Li, Chaochao Chen, Xiaolin Zheng +3
Recent regulations on the Right to be Forgotten have greatly influenced the way of running a recommender system, because users now have the right to withdraw their private data. Be…
Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation
Yuyuan Li, Xiaolin Zheng, Chaochao Chen +1
Privacy laws and regulations enforce data-driven systems, e.g., recommender systems, to erase the data that concern individuals. As machine learning models potentially memorize the…