2 citations · 3 across the 14 of their papers we have counts for
9 papers · 1 filter
Taming the Long Tail: Efficient Item-wise Sharpness-Aware Minimization for LLM-based Recommender Systems
Jiaming Zhang, Yuyuan Li, Xiaohua Feng +4
Large Language Model-based Recommender Systems (LRSs) have recently emerged as a new paradigm in sequential recommendation by directly adopting LLMs as backbones. While LRSs demons…
FedAU2: Attribute Unlearning for User-Level Federated Recommender Systems with Adaptive and Robust Adversarial Training
Yuyuan Li, Junjie Fang, Fengyuan Yu +7
Federated Recommender Systems (FedRecs) leverage federated learning to protect user privacy by retaining data locally. However, user embeddings in FedRecs often encode sensitive at…
UFO: Unfair-to-Fair Evolving Mitigates Unfairness in LLM-based Recommender Systems via Self-Play Fine-tuning
Jiaming Zhang, Yuyuan Li, Xiaohua Feng +3
Large language model-based Recommender Systems (LRSs) have demonstrated superior recommendation performance by integrating pre-training with Supervised Fine-Tuning (SFT). However,…
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…
RAID: An In-Training Defense against Attribute Inference Attacks in Recommender Systems
Xiaohua Feng, Yuyuan Li, Fengyuan Yu +5
In various networks and mobile applications, users are highly susceptible to attribute inference attacks, with particularly prevalent occurrences in recommender systems. Attackers…
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.…