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cs.IR2025
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…
cs.IR2025
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…
cs.IR2025★ 1 cited
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…