25 citations · 32 across the 7 of their papers we have counts for
7 papers
R2LED: Equipping Retrieval and Refinement in Lifelong User Modeling with Semantic IDs for CTR Prediction
Qidong Liu, Gengnan Wang, Zhichen Liu +6
Lifelong user modeling, which leverages users' long-term behavior sequences for CTR prediction, has been widely applied in personalized services. Existing methods generally adopted…
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…
Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach
Chunxu Zhang, Guodong Long, Hongkuan Guo +5
Multifaceted user modeling aims to uncover fine-grained patterns and learn representations from user data, revealing their diverse interests and characteristics, such as profile, p…
GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching
Xiao Han, Zijian Zhang, Xiangyu Zhao +6
As urban residents demand higher travel quality, vehicle dispatch has become a critical component of online ride-hailing services. However, current vehicle dispatch systems struggl…
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…