Over the past decades, recommendation has become a critical component of many online services such as media streaming and e-commerce. Recent advances in algorithms, evaluation methods and datasets have led to continuous improvements of the state-of-the-art. However, much work remains to be done to make these methods scale to the size of the internet. Online advertising offers a unique testbed for recommendation at scale. Every day, billions of users interact with millions of products in real-time. Systems addressing this scenario must work reliably at scale. We propose an efficient model (LED, for Lightweight Encoder-Decoder) reaching a new trade-off between complexity, scale and performance. Specifically, we show that combining large-scale matrix factorization with lightweight embedding fine-tuning unlocks state-of-the-art performance at scale. We further provide the detailed description of a system architecture and demonstrate its operation over two months at the scale of the internet. Our design allows serving billions of users across hundreds of millions of items in a few milliseconds using standard hardware.

中文翻译：

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展示广告的可扩展表示学习和检索

在过去的几十年中，推荐已成为许多在线服务（例如媒体流和电子商务）的重要组成部分。算法，评估方法和数据集的最新进展已导致对最新技术的不断改进。但是，要使这些方法扩展到Internet的规模，还有许多工作要做。在线广告为大规模推荐提供了独特的测试平台。每天，数十亿用户与数百万种产品进行实时交互。解决此情况的系统必须可靠地大规模运行。我们提出了一种有效的模型（LED，用于轻型编码器/解码器），在复杂性，规模和性能之间取得了新的折衷。具体而言，我们证明了将大规模矩阵分解与轻量级嵌入微调相结合，可以释放出大规模的最新性能。我们进一步提供了系统体系结构的详细说明，并演示了两个月来以互联网规模运行的情况。我们的设计允许使用标准硬件在数毫秒内为数亿个项目的数十亿用户提供服务。