On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models






For industrial-scale advertising systems, prediction of ad click- through rate (CTR) is a central problem. Ad clicks constitute a significant class of user engagements and are often used as the primary signal for the usefulness of ads to users. Additionally, in cost-per-click advertising systems where advertisers are charged per click, click rate expectations feed directly into value estimation. Accordingly, CTR model development is a significant investment for most Internet advertising companies. Engineering for such prob- lems requires many machine learning (ML) techniques suited to online learning that go well beyond traditional accuracy improve- ments, especially concerning efficiency, reproducibility, calibration, credit attribution. We present a case study of practical techniques deployed in Google’s search ads CTR model. This paper provides an industry case study highlighting important areas of current ML research and illustrating how impactful new ML methods are evaluated and made useful in a large-scale industrial setting.


为了解决这类问题,需要许多适合在线学习的机器学习技术,不同于用于提升传统机器学习模型准确率的技巧,其更关注于效率、可重复性、校准和归因(credit attribution,简单翻译为归因可能不太准确,后文会详细解释)。