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Found 29 papers, 10 papers with code
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards.
Future Impact Decomposition in Request-level Recommendations
Furthermore, we show that a reward-based future decomposition strategy can better express the item-wise future impact and improve the recommendation accuracy in the long term.
AdaRec: Adaptive Sequential Recommendation for Reinforcing Long-term User Engagement
One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies.
KuaiSim: A Comprehensive Simulator for Recommender Systems
For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research.
A Large Language Model Enhanced Conversational Recommender System
For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.
State Regularized Policy Optimization on Data with Dynamics Shift
Data with dynamics shift are separated according to their environment parameters to train the corresponding policy.
Generative Flow Network for Listwise Recommendation
In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality.
Exploration and Regularization of the Latent Action Space in Recommendation
To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.
Multi-Task Recommendations with Reinforcement Learning
To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.
Reinforcing User Retention in a Billion Scale Short Video Recommender System
In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance.
Two-Stage Constrained Actor-Critic for Short Video Recommendation
One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning.
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement
Though promising, the application of RL heavily relies on well-designed rewards, but designing rewards related to long-term user engagement is quite difficult.
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequential recommendation.
Constrained Reinforcement Learning for Short Video Recommendation
In this paper, we formulate the problem of short video recommendation as a constrained Markov Decision Process (MDP), where platforms want to optimize the main goal of user watch time in long term, with the constraint of accommodating the auxiliary responses of user interactions such as sharing/downloading videos.
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation
In this paper, we propose a novel semi-supervised learning (SSL) framework named BoostMIS that combines adaptive pseudo labeling and informative active annotation to unleash the potential of medical image SSL models: (1) BoostMIS can adaptively leverage the cluster assumption and consistency regularization of the unlabeled data according to the current learning status.
MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioning
We propose the Multimodal relAtional Graph adversarIal inferenCe (MAGIC) framework for diverse and unpaired TextCap.
A Survey on Deep Reinforcement Learning for Data Processing and Analytics
Data processing and analytics are fundamental and pervasive.
Softmax Deep Double Deterministic Policy Gradients
A widely-used actor-critic reinforcement learning algorithm for continuous control, Deep Deterministic Policy Gradients (DDPG), suffers from the overestimation problem, which can negatively affect the performance.
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce
In this paper, we present a novel Generator and Critic slate re-ranking approach, where the Critic evaluates the slate and the Generator ranks the items by the reinforcement learning approach.
Multi-Path Policy Optimization
Recent years have witnessed a tremendous improvement of deep reinforcement learning.
Deterministic Value-Policy Gradients
Based on this theoretical guarantee, we propose a class of the deterministic value gradient algorithm (DVG) with infinite horizon, and different rollout steps of the analytical gradients by the learned model trade off between the variance of the value gradients and the model bias.
Reinforcement Learning Driven Heuristic Optimization
In this paper, we introduce a novel framework to generate better initial solutions for heuristic algorithms using reinforcement learning (RL), named RLHO.
Reinforcement Learning with Dynamic Boltzmann Softmax Updates
In this paper, we propose to update the value function with dynamic Boltzmann softmax (DBS) operator, which has good convergence property in the setting of planning and learning.
Policy Optimization with Model-based Explorations
Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games.
A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning
We then propose the dynamic Boltzmann softmax(DBS) operator to enable the convergence to the optimal value function in value iteration.
Deterministic Policy Gradients With General State Transitions
Such a setting generalizes the widely-studied stochastic state transition setting, namely the setting of deterministic policy gradient (DPG).
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module.
Policy Gradients for Contextual Recommendations
We evaluate PGCR on toy datasets as well as a real-world dataset of personalized music recommendations.
Reinforcement Mechanism Design for e-commerce
We study the problem of allocating impressions to sellers in e-commerce websites, such as Amazon, eBay or Taobao, aiming to maximize the total revenue generated by the platform.
