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Found 21 papers, 2 papers with code
Uncertainty-aware Distributional Offline Reinforcement Learning
Offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data.
Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems
Reinforcement Learning-based Recommender Systems (RLRS) have shown promise across a spectrum of applications, from e-commerce platforms to streaming services.
On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems
Offline reinforcement learning empowers agents to glean insights from offline datasets and deploy learned policies in online settings.
Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation
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.
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning
Reinforcement learning-based recommender systems have recently gained popularity.
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation
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.
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems
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.
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation
Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems.
Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation
Recent advances have convinced that the ability of reinforcement learning to handle the dynamic process can be effectively applied in the interactive recommendation.
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems
Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.
Locality-Sensitive Experience Replay for Online Recommendation
Existing studies encourage the agent to learn from past experience via experience replay (ER).
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions
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.
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference
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.
Generative Adversarial U-Net for Domain-free Medical Image Augmentation
The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing.
Generative Inverse Deep Reinforcement Learning for Online Recommendation
Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process.
Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images
Diagnosis from chest CT images is a promising direction.
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems
Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy.
Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images
The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy.
Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals
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.
Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning
Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e. g., computer version).
