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.
The process of determining which ad to show to the Snapchatter consists of multiple steps:
- Ad eligibility filtering: As the first stage, we perform ad targeting, budget checks and other filtering steps, including privacy and ads policy compliance. This determines which ads are eligible for a given Snapchatter out of the entire collection of ad inventory.
- Candidate generation: lightweight ML models then cull a smaller set of candidate ads (typically hundreds to a few thousand). The goal here is to maximize the recall for the next stage heavy ML models.
- Heavy ML models: for each candidate ad, these models generate scores such as the probability of conversion after seeing an ad and the estimated organic utility of the ad.
- Auction: finally, the scores from the ML models, advertisers’ bids for the ads, remaining budgets for the ads, and various business rules are used to run an auction that generates the final value for each ad and selects the highest value ad. This winner ad is then shown to Snapchatter.
- Feedback loop: interactions with the ad in turn generate training data for the ML models.
The ML specific development goes through many logical steps such as offline experimentation, benchmarking and deployment for online inference, online A/B testing, continuous updates of models and performance monitoring. These are enabled by custom platforms and supporting infrastructure.
Ad ranking for Snapchat provides the right scale and business impact potential to continuously develop and apply state of the art ML algorithms and infrastructure.
Through this article, we intend to share an overview of our ad-marketplace, the role ML plays in ad ranking, the challenges unique to ML for ad ranking, and various components of the end-to-end ML development cycle.
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