RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems

no code implementations • 20 Sep 2024 • Shuo Su, Xiaoshuang Chen, Yao Wang, Yulin Wu, Ziqiang Zhang, Kaiqiao Zhan, Ben Wang, Kun Gai

Then, we propose a reinforcement prediction-allocation framework (RPAF) to address these issues.

Recommendation Systems

Cache-Aware Reinforcement Learning in Large-Scale Recommender Systems

no code implementations • 23 Apr 2024 • Xiaoshuang Chen, Gengrui Zhang, Yao Wang, Yulin Wu, Shuo Su, Kaiqiao Zhan, Ben Wang

The recommendation with a cache is a solution to this problem, where a user-wise result cache is used to provide recommendations when the recommender system cannot afford a real-time computation.

Recommendation Systems reinforcement-learning +1

CREAD: A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems

no code implementations • 15 Jan 2024 • Jie Sun, Zhaoying Ding, Xiaoshuang Chen, Qi Chen, Yincheng Wang, Kaiqiao Zhan, Ben Wang

These results highlight the effectiveness of the CREAD framework in watch time prediction in video recommender systems.

Recommendation Systems

UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution

no code implementations • 12 Jan 2024 • Gengrui Zhang, Yao Wang, Xiaoshuang Chen, Hongyi Qian, Kaiqiao Zhan, Ben Wang

In recent years, there has been a growing interest in utilizing reinforcement learning (RL) to optimize long-term rewards in recommender systems.

Multi-agent Reinforcement Learning Recommendation Systems +3