DoorDash用深度学习提升配送广告转化预测，推动推荐系统升级
Applying Deep Learning to Ads Conversion Prediction in Last Mile Delivery Marketplace
- 将广告排名从传统树模型升级为多任务深度神经网络
- 显著提升转化预测效果，带来显著业务收益
- 适合关注工业级推荐系统落地的团队参考
深度神经网络（DNN）已彻底改变大规模排名系统，助力捕捉复杂用户行为并实现性能突破。在DoorDash，我们首次将这一变革力量应用于首页广告排序系统，将传统基于树的模型升级为先进的多任务DNN。此次演进推动了数据基础、模型设计、训练效率、评估严谨性和在线服务等方面的进步，带来了显著的业务影响，并重塑了机器学习系统的构建方式。本文讲述了一段以问题为导向的实践旅程，涵盖问题识别、针对性解决方案设计，以及应对深度学习推荐系统开发与扩展复杂性的挑战。通过成功经验和教训，旨在为追求类似机器学习系统升级的团队提供实用洞见与指导。
原文摘要 · Abstract (English)
Deep neural networks (DNNs) have revolutionized web-scale ranking systems, enabling breakthroughs in capturing complex user behaviors and driving performance gains. At DoorDash, we first harnessed this transformative power by transitioning our homepage Ads ranking system from traditional tree based models to cutting edge multi task DNNs. This evolution sparked advancements in data foundations, model design, training efficiency, evaluation rigor, and online serving, delivering substantial business impact and reshaping our approach to machine learning. In this paper, we talk about our problem driven journey, from identifying the right problems and crafting targeted solutions to overcoming the complexity of developing and scaling a deep learning recommendation system. Through our successes and learned lessons, we aim to share insights and practical guidance to teams pursuing similar advancements in machine learning systems.
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