19 citations · 30 across the 11 of their papers we have counts for
7 papers · 1 filter
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 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…
IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation
Jing Du, Zesheng Ye, Lina Yao +2
Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised…
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems
Guanglin Zhou, Chengkai Huang, Xiaocong Chen +4
The field of generating recommendations within the framework of causal inference has seen a recent surge, with recommendations being likened to treatments. This approach enhances i…
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…