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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…
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…