4 papers
Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction
Hongtao Huang, Chengkai Huang, Junda Wu +3
Forecasting multi-step user behavior trajectories requires reasoning over structured preferences across future actions, a challenge overlooked by traditional sequential recommendat…
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Ruhan Wang, Zhiyong Wang, Chengkai Huang +5
For question-answering (QA) tasks, in-context learning (ICL) enables language models to generate responses without modifying their parameters by leveraging examples provided in the…
A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms
Chengkai Huang, Hongtao Huang, Tong Yu +6
Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions betwee…
DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents
Shiyi Yang, Zhibo Hu, Xinshu Li +5
Large language model (LLM)-powered agents are increasingly used in recommender systems (RSs) to achieve personalized behavior modeling, where the memory mechanism plays a pivotal r…