Xiaocong Chen

Advantage-Conditioned Flow Policy for Offline Reinforcement Learning in Recommendation

Xiaocong Chen, Siyu Wang, Lina Yao

Offline reinforcement learning (RL) is a useful approach for recommender systems because it can optimize long-term user feedback from logged interaction data without online exploration. A key challenge is the multi-modal nature of user preferences: a user may like several unrelated item …

au (code pays fourni par la source)

Microplastics and bisphenol A exposure induce hepatopancreatic damage and lipid metabolism disorders in Portunus trituberculatus

Tao Zhang, Biqing Wen, Xunjie Huo, Xiaoyue Ge et autres

cn (code pays fourni par la source)

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, particularly in offline settings, where data inefficiency and reliance on pre-collected trajectories limit their broader applicability. While offline reinforcement learning …

au (code pays fourni par la source)

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 user feedback data can be sub-optimal or sparse. In this paper, we introduce MDT4Rec, an offline RLRS framework that builds …

au (code pays fourni par la source)

Diffusion Policies for Risk-Averse Behavior Modeling in Offline Reinforcement Learning

Xiaocong Chen, Siyu Wang, Tong Yu, Lina Yao

Offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data. A central concern in this context is ensuring the safety of the learned policy by quantifying uncertainties associated with various actions and environmental stochasticity. Traditional approaches primarily emphasize …

au, us (code pays fourni par la source)

A Bio-inspired Bubble Artificial Muscles and TacTip Perception-driven Tri-legged Robot for Obstacle Avoidance

Chaoqun Xiang, Zhengwei Zhong, Wenqiang Wu, Xiaocong Chen et autres

cn, gb (code pays fourni par la source)

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, particularly in offline settings, where data inefficiency and reliance on pre-collected trajectories limit their broader applicability. While offline reinforcement learning …

Identification of key genes associated with infertile endometriosis based on bioinformatic analysis

Hongjuan Ye, Min Hou, Xin Xie, Yang Wang et autres

Background: Endometriosis is a common disease among women of childbearing age. However, the molecular mechanism behind it is still unknown. Therefore, new biomarkers and therapeutic targets are needed to improve the diagnosis and treatment of infertile women. Methods: Microarray datasets GSE7305, GSE7307, …

cn (code pays fourni par la source)

GDC: A Hybrid Method for Hierarchical Data Grading and Classification

Chao Han, Wumo Pan, Ke Huang, Shengjie Zhai et autres

With the continuous expansion of big data scales, traditional single data grading and classification algorithms face challenges in handling complex data structures and achieving efficient querying. This paper proposes GDC, a hybrid method combining hierarchical clustering and decision trees. The method first …

cn (code pays fourni par la source)

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-term rewards. However, raw state representations often contain high-dimensional, noisy information and components that are not causally relevant to the reward. Additionally, …

au (code pays fourni par la source)

FK506 activates the BMP signaling pathway to regulate ovarian development in Portunus trituberculatus

Xiaocong Chen, Ce Shi, Yangfang Ye, Chunlin Wang et autres

cn (code pays fourni par la source)

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 with challenges, notably in crafting reward functions and harnessing large pre-existing datasets within the RL framework. Recent advancements in offline …

au (code pays fourni par la source)