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arXiv · 2101.00870

Lightweight representation learning for efficient and scalable recommendation

Olivier Koch·Amine Benhalloum·Guillaume Genthial·Denis Kuzin·Dmitry Parfenchik
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Abstract

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.

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Olivier Koch, Amine Benhalloum, Guillaume Genthial, Denis Kuzin, Dmitry Parfenchik. 2021-01-04. Lightweight representation learning for efficient and scalable recommendation. https://arxiv.org/abs/2101.00870

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