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4 months ago
Machine Learning for Snapchat Ad Ranking

 
Originally published in Snapchat Engineering, July 11, 2022.
Snapchat ad ranking aims to serve the right ad to the right user at the right time.  These are selected from millions of ads in our inventory at any time. We do so with a strong emphasis on maintaining an excellent user experience and upholding Snap’s strong privacy principles and security standards, including honoring user privacy choices. Serving the right ad, in turn, generates value for our community of advertisers and Snapchatters. Under the hood, a very high throughput real-time ad auction is powered by large-scale distributed engineering systems and state of the art deep learning ML models.

 

This post details an overview of the Snapchat ad ranking system, the challenges unique to the online ad ecosystem, and the corresponding machine learning (ML) development cycle.

 

Snapchat ad ranking aims to serve the right ad to the right user at the right time.  These are selected from millions of ads in our inventory at any time. We do so with a strong emphasis on maintaining an excellent user experience and upholding Snap’s strong privacy principles and security standards, including honoring user privacy choices. Serving the right ad, in turn, generates value for our community of advertisers and Snapchatters. Under the hood, a very high throughput real-time ad auction is powered by large-scale distributed engineering systems and state of the art deep learning ML models.

 

This post details an overview of the Snapchat ad ranking system, the challenges unique to the online ad ecosystem, and the corresponding machine learning (ML) development cycle.

 

To continue reading this article, click here.

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