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pro vyhledávání: '"Maurya, Kailash Singh"'
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 platform with multiple continuous parameters influencing key performan
Externí odkaz:
http://arxiv.org/abs/2410.03697
Autor:
Jia, Yimeng, Paneri, Kaushal, Huang, Rong, Maurya, Kailash Singh, Mallapragada, Pavan, Shi, Yifan
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 online ad auctions. Traditional importance sampling (IS) methods face c
Externí odkaz:
http://arxiv.org/abs/2409.13655