187 citations · 521 across the 19 of their papers we have counts for
11 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 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…
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…
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…
Locality-Sensitive Experience Replay for Online Recommendation
Xiaocong Chen, Lina Yao, Xianzhi Wang +1
Online recommendation requires handling rapidly changing user preferences. Deep reinforcement learning (DRL) is gaining interest as an effective means of capturing users' dynamic i…
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions
Xiaocong Chen, Lina Yao, Julian McAuley +2
In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely an…
Generative Inverse Deep Reinforcement Learning for Online Recommendation
Xiaocong Chen, Lina Yao, Aixin Sun +3
Deep reinforcement learning enables an agent to capture user's interest through interactions with the environment dynamically. It has attracted great interest in the recommendation…
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
Xiaocong Chen, Chaoran Huang, Lina Yao +3
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous…