5 citations · 6 across the 6 of their papers we have counts for
8 papers
Controllable Generative Recommendation via Guided Token Refinement
Haichao Zhang, Zhixuan Liang, Chong Zhang +3
Generative recommendation over discrete Semantic IDs has become a powerful paradigm for item retrieval. Real-world systems, however, frequently require real-time controllability to…
PsyBer-Agent: A Psychology-Driven User Agent for Recommender Systems
Chong Zhang, Qijie Shen, Haichao Zhang +3
Evaluating recommender systems before deployment requires simulators that remain realistic over long, closed-loop interaction cycles. Current LLM-based agents generally function as…
Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation
Daiwei Chen, Zhoutong Fu, Chengming Jiang +12
Language models (LMs) are increasingly extended with new learnable vocabulary tokens for domain-specific tasks, such as Semantic-ID tokens in generative recommendation. The standar…
Customized Retrieval-Augmented Generation with LLM for Debiasing Recommendation Unlearning
Haichao Zhang, Chong Zhang, Peiyu Hu +2
Modern recommender systems face a critical challenge in complying with privacy regulations like the 'right to be forgotten': removing a user's data without disrupting recommendatio…
Uncertainty-Aware Semantic Decoding for LLM-Based Sequential Recommendation
Chenke Yin, Li Fan, Jia Wang +4
Large language models have been widely applied to sequential recommendation tasks, yet during inference, they continue to rely on decoding strategies developed for natural language…
LLM4Rail: An LLM-Augmented Railway Service Consulting Platform
Zhuo Li, Xianghuai Deng, Chiwei Feng +7
Large language models (LLMs) have significantly reshaped different walks of business. To meet the increasing demands for individualized railway service, we develop LLM4Rail - a nov…
Policy Expansion for Bridging Offline-to-Online Reinforcement Learning
Haichao Zhang, We Xu, Haonan Yu
Pre-training with offline data and online fine-tuning using reinforcement learning is a promising strategy for learning control policies by leveraging the best of both worlds in te…
Do You Need the Entropy Reward (in Practice)?
Haonan Yu, Haichao Zhang, Wei Xu
Maximum entropy (MaxEnt) RL maximizes a combination of the original task reward and an entropy reward. It is believed that the regularization imposed by entropy, on both policy imp…