2 papers
cs.LG2024
Combining Open-box Simulation and Importance Sampling for Tuning Large-Scale Recommenders
Kaushal Paneri, Michael Munje, Kailash Singh Maurya +2
Growing scale of recommender systems require extensive tuning to respond to market dynamics and system changes. We address the challenge of tuning a large-scale ads recommendation…
cs.LG2024
Adaptive Mixture Importance Sampling for Automated Ads Auction Tuning
Yimeng Jia, Kaushal Paneri, Rong Huang +3
This paper introduces Adaptive Mixture Importance Sampling (AMIS) as a novel approach for optimizing key performance indicators (KPIs) in large-scale recommender systems, such as o…