1 citations · 7 across the 13 of their papers we have counts for
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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…
On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems
Xiaocong Chen, Siyu Wang, Julian McAuley +2
Reinforcement learning serves as a potent tool for modeling dynamic user interests within recommender systems, garnering increasing research attention of late. However, a significa…
Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation
Siyu Wang, Xiaocong Chen, Quan Z. Sheng +2
Recommendation models are typically trained on observational user interaction data, but the interactions between latent factors in users' decision-making processes lead to complex…
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning
Siyu Wang, Xiaocong Chen, Dietmar Jannach +1
Reinforcement learning-based recommender systems have recently gained popularity. However, the design of the reward function, on which the agent relies to optimize its recommendati…
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation
Xiaocong Chen, Siyu Wang, Lina Yao +2
Deep reinforcement learning (DRL) has been proven its efficiency in capturing users' dynamic interests in recent literature. However, training a DRL agent is challenging, because o…
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation
Siyu Wang, Xiaocong Chen, Lina Yao +2
Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems. How…
Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation
Siyu Wang, Xiaocong Chen, Lina Yao
Interactive recommendation is able to learn from the interactive processes between users and systems to confront the dynamic interests of users. Recent advances have convinced that…