8 papers
PRISM: Personalized Recommendation via Information Synergy Module
Xinyi Zhang, Yutong Li, Peijie Sun +2
Multimodal sequential recommendation (MSR) leverages diverse item modalities to improve recommendation accuracy, while achieving effective and adaptive fusion remains challenging.…
TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Models
Li Zhang, Zhongxuan Han, XiaoHua Feng +5
Efficient and lightweight adaptation of pre-trained Vision-Language Models (VLMs) to downstream tasks through collaborative interactions between local clients and a central server…
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
Li Zhang, Zhongxuan Han, Xiaohua Feng +3
With the emerging application of Federated Learning (FL) in decision-making scenarios, it is imperative to regulate model fairness to prevent disparities across sensitive groups (e…
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…
LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning
Li Zhang, Chaochao Chen, Zhongxuan Han +2
Federated learning (FL) has garnered considerable interest for its capability to learn from decentralized data sources. Given the increasing application of FL in decision-making sc…