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Multistage feed ranking system with methodology providing scoring model optimization for scaling
Multistage feed ranking system with methodology providing scoring model optimization for scaling
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机译:具有方法的多级饲料排名系统,提供缩放的评分模型优化
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摘要
A feature importance score for a target machine learning feature of a target machine learning model used in a multistage feed ranking system for scoring feed items is supplemented with a feature computing resource cost. The feature computing resource cost represents the cost of using the target feature in the target model in terms of computing resources such as CPU, memory, network resources, etc. A tradeoff between feature importance and feature computing resource cost can be made to decide whether to have the target machine learning model use or not use the target machine learning feature in production, thereby improving the production multistage feed item ranking system and solving the technical problem of determining which machine learning features of a machine learning model represent the best tradeoff between feature importance and feature computing resource cost.
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