2 papers
cs.LG2024
RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems
Shuo Su, Xiaoshuang Chen, Yao Wang +5
Modern recommender systems are built upon computation-intensive infrastructure, and it is challenging to perform real-time computation for each request, especially in peak periods,…
cs.IR2024
Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems
Kai Zheng, Haijun Zhao, Rui Huang +6
The Probability Ranking Principle (PRP) has been considered as the foundational standard in the design of information retrieval (IR) systems. The principle requires an IR module's…