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.

翻译：深度神经网络（DNNs）已彻底革新了网络规模的排序系统，在捕捉复杂用户行为和推动性能提升方面实现了突破。在DoorDash，我们首次利用这种变革性力量，将主页广告排序系统从传统的基于树的模型转变为尖端的多任务DNNs。这一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方面的进步，带来了显著的商业影响，并重塑了我们处理机器学习的方法。在本文中，我们探讨了以问题为导向的探索历程，从识别正确的问题、制定针对性解决方案，到克服开发和扩展深度学习推荐系统的复杂性。通过我们的成功经验与所学教训，我们旨在为在机器学习系统中追求类似进步的团队分享见解和实用指导。