4 papers
Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation
Jiajie Su, Qiyong Zhong, Yunshan Ma +5
Session-based recommendation (SBR) predicts the next item based on anonymous sessions. Traditional SBR explores user intents based on ID collaborations or auxiliary content. To fur…
WassFFed: Wasserstein Fair Federated Learning
Zhongxuan Han, Li Zhang, Chaochao Chen +4
Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training da…
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…
Integration of Large Language Models and Federated Learning
Chaochao Chen, Xiaohua Feng, Yuyuan Li +4
As the parameter size of Large Language Models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has…