no code implementations • 4 Feb 2025 • Siyu Wang, Xiaocong Chen, Lina Yao
This policy is guided by a reward function based on the Wasserstein distance, which measures the causal effect of state components on the reward and encourages the preservation of CRCs that directly influence user interests.
no code implementations • 18 Jul 2024 • Siyu Wang, Xiaocong Chen, Lina Yao
To tackle this problem, we introduce an innovative causal approach for decomposing the state and extracting \textbf{C}ausal-\textbf{I}n\textbf{D}ispensable \textbf{S}tate Representations (CIDS) in RLRS.
no code implementations • 2 Jun 2024 • Xiaocong Chen, Siyu Wang, Lina Yao
In response, we introduce a novel methodology named Max-Entropy enhanced Decision Transformer with Reward Relabeling for Offline RLRS (EDT4Rec).
no code implementations • 26 Mar 2024 • Xiaocong Chen, Siyu Wang, Tong Yu, Lina Yao
Offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data.
no code implementations • 26 Mar 2024 • 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.
no code implementations • 22 Aug 2023 • Xiaocong Chen, Siyu Wang, Julian McAuley, Dietmar Jannach, Lina Yao
Offline reinforcement learning empowers agents to glean insights from offline datasets and deploy learned policies in online settings.
no code implementations • 17 Apr 2023 • Siyu Wang, Xiaocong Chen, Dietmar Jannach, Lina Yao
Reinforcement learning-based recommender systems have recently gained popularity.
no code implementations • 17 Apr 2023 • Siyu Wang, Xiaocong Chen, Quan Z. Sheng, Yihong Zhang, Lina Yao
This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems.
no code implementations • 17 Sep 2022 • Xiaocong Chen, Siyu Wang, Lina Yao, Lianyong Qi, Yong Li
It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems.
no code implementations • 13 Aug 2022 • Guanglin Zhou, Chengkai Huang, Xiaocong Chen, Xiwei Xu, Chen Wang, Liming Zhu, Lina Yao
Recognizing that confounders may be elusive, we propose a contrastive self-supervised learning to minimize exposure bias, employing inverse propensity scores and expanding the positive sample set.
no code implementations • 10 Aug 2022 • Siyu Wang, Xiaocong Chen, Lina Yao, Sally Cripps, Julian McAuley
Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems.
no code implementations • 1 Apr 2022 • Siyu Wang, Xiaocong Chen, Lina Yao
Recent advances have convinced that the ability of reinforcement learning to handle the dynamic process can be effectively applied in the interactive recommendation.
no code implementations • 2 Dec 2021 • Siyu Wang, Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi Wang, Quan Z. Sheng
Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.
no code implementations • 21 Oct 2021 • Xiaocong Chen, Lina Yao, Xianzhi Wang, Julian McAuley
Existing studies encourage the agent to learn from past experience via experience replay (ER).
no code implementations • 8 Sep 2021 • Xiaocong Chen, Lina Yao, Julian McAuley, Guanglin Zhou, Xianzhi Wang
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 and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems.
no code implementations • 3 May 2021 • Xiaocong Chen, Lina Yao, Xianzhi Wang, Aixin Sun, Wenjie Zhang, Quan Z. Sheng
Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e. g., in reinforcement learning based recommender systems.
no code implementations • 12 Jan 2021 • Xiaocong Chen, Yun Li, Lina Yao, Ehsan Adeli, Yu Zhang
The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing.
no code implementations • 4 Nov 2020 • Xiaocong Chen, Lina Yao, Aixin Sun, Xianzhi Wang, Xiwei Xu, Liming Zhu
Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process.
no code implementations • 16 Jun 2020 • Xiaocong Chen, Lina Yao, Tao Zhou, Jinming Dong, Yu Zhang
Diagnosis from chest CT images is a promising direction.
no code implementations • 14 Jun 2020 • Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi Wang, Wei Emma Zhang
Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.
no code implementations • 17 Apr 2020 • Xiaocong Chen, Chaoran Huang, Lina Yao, Xianzhi Wang, Wei Liu, Wenjie Zhang
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy.
no code implementations • 12 Apr 2020 • Xiaocong Chen, Lina Yao, Yu Zhang
The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy.
2 code implementations • 31 Jul 2019 • Xiang Zhang, Xiaocong Chen, Manqing Dong, Huan Liu, Chang Ge, Lina Yao
In light of this, we propose a novel multi-task generative adversarial network to convert the individual's EEG signals evoked by geometrical shapes to the original geometry.
1 code implementation • 31 Jul 2019 • Xiang Zhang, Xiaocong Chen, Lina Yao, Chang Ge, Manqing Dong
Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e. g., computer version).