25 citations · 79 across the 14 of their papers we have counts for
14 papers
Phase-Aware Mixture of Experts for Agentic Reinforcement Learning
Shengtian Yang, Yu Li, Shuo He +4
Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a \emph{single} policy network, causing…
MindRec: A Diffusion-driven Coarse-to-Fine Paradigm for Generative Recommendation
Mengyao Gao, Chongming Gao, Haoyan Liu +5
Recent advancements in large language model-based recommendation systems often represent items as text or semantic IDs and generate recommendations in an auto-regressive manner. Ho…
From Principles to Applications: A Comprehensive Survey of Discrete Tokenizers in Generation, Comprehension, Recommendation, and Information Retrieval
Jian Jia, Jingtong Gao, Ben Xue +6
Discrete tokenizers have emerged as indispensable components in modern machine learning systems, particularly within the context of autoregressive modeling and large language model…
Value Function Decomposition in Markov Recommendation Process
Xiaobei Wang, Shuchang Liu, Qingpeng Cai +4
Recent advances in recommender systems have shown that user-system interaction essentially formulates long-term optimization problems, and online reinforcement learning can be adop…
Future-Conditioned Recommendations with Multi-Objective Controllable Decision Transformer
Chongming Gao, Kexin Huang, Ziang Fei +6
Securing long-term success is the ultimate aim of recommender systems, demanding strategies capable of foreseeing and shaping the impact of decisions on future user satisfaction. C…
LLM-Powered User Simulator for Recommender System
Zijian Zhang, Shuchang Liu, Ziru Liu +6
User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerati…
GAS: Generative Auto-bidding with Post-training Search
Yewen Li, Shuai Mao, Jingtong Gao +6
Auto-bidding is essential in facilitating online advertising by automatically placing bids on behalf of advertisers. Generative auto-bidding, which generates bids based on an adjus…
DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems
Jiaju Chen, Chongming Gao, Shuai Yuan +3
The integration of Large Language Models (LLMs) into recommender systems has led to substantial performance improvements. However, this often comes at the cost of diminished recomm…
Modeling User Retention through Generative Flow Networks
Ziru Liu, Shuchang Liu, Bin Yang +7
Recommender systems aim to fulfill the user's daily demands. While most existing research focuses on maximizing the user's engagement with the system, it has recently been pointed…
M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework
Zijian Zhang, Shuchang Liu, Jiaao Yu +9
Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehen…
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
Ziru Liu, Shuchang Liu, Zijian Zhang +6
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-t…
Future Impact Decomposition in Request-level Recommendations
Xiaobei Wang, Shuchang Liu, Xueliang Wang +6
In recommender systems, reinforcement learning solutions have shown promising results in optimizing the interaction sequence between users and the system over the long-term perform…
KuaiSim: A Comprehensive Simulator for Recommender Systems
Kesen Zhao, Shuchang Liu, Qingpeng Cai +5
Reinforcement Learning (RL)-based recommender systems (RSs) have garnered considerable attention due to their ability to learn optimal recommendation policies and maximize long-ter…
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems
Zhenghai Xue, Qingpeng Cai, Bin Yang +4
The field of Reinforcement Learning (RL) has garnered increasing attention for its ability of optimizing user retention in recommender systems. A primary obstacle in this optimizat…