1 citations · 3 across the 7 of their papers we have counts for
7 papers
Maximum In-Support Return Modeling for Dynamic Recommendation with Language Model Prior
Xiaocong Chen, Siyu Wang, Lina Yao
Reinforcement Learning-based recommender systems (RLRS) offer an effective way to handle sequential recommendation tasks but often face difficulties in real-world settings, where u…
Energy-Guided Diffusion Sampling for Long-Term User Behavior Prediction in Reinforcement Learning-based Recommendation
Xiaocong Chen, Siyu Wang, Lina Yao
Reinforcement learning-based recommender systems (RL4RS) have gained attention for their ability to adapt to dynamic user preferences. However, these systems face challenges, parti…
Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation
Siyu Wang, Xiaocong Chen, Lina Yao
In offline reinforcement learning-based recommender systems (RLRS), learning effective state representations is crucial for capturing user preferences that directly impact long-ter…
On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems
Siyu Wang, Xiaocong Chen, Lina Yao
In Reinforcement Learning-based Recommender Systems (RLRS), the complexity and dynamism of user interactions often result in high-dimensional and noisy state spaces, making it chal…
Maximum-Entropy Regularized Decision Transformer with Reward Relabelling for Dynamic Recommendation
Xiaocong Chen, Siyu Wang, Lina Yao
Reinforcement learning-based recommender systems have recently gained popularity. However, due to the typical limitations of simulation environments (e.g., data inefficiency), most…
Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems
Siyu Wang, Xiaocong Chen, Lina Yao
Reinforcement Learning-based Recommender Systems (RLRS) have shown promise across a spectrum of applications, from e-commerce platforms to streaming services. Yet, they grapple wit…
Diffusion Policies for Risk-Averse Behavior Modeling in Offline Reinforcement Learning
Xiaocong Chen, Siyu Wang, Tong Yu +1
Offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data. A central concern in this context is ensuring the safety of the learned…