Architectural Components
Understand the architectural components of an ad prediction system. Learn how advertisers input targeting rules, how user queries trigger ad selection, and how machine learning models predict user engagement. Discover the auction mechanism that ranks ads by relevance and bid, pacing strategies to optimize budget spend, and the funnel approach for efficient large-scale ad prediction. This lesson prepares you to design scalable, practical ML-based ad systems with an emphasis on relevance and performance.
We'll cover the following...
Architecture
Let’s have a look at the high-level architecture of the system. There will be two main actors involved in our ad prediction system - platform users and advertiser. Let’s see how they fit in the architecture:
1. Advertiser flow
Advertisers create ads containing their content as well as targeting, i.e., scenarios in which they want to trigger their ads. A few examples are:
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Query-based targeting: This method shows ads to the user based on the query terms. The query terms can be a partial match, full match, expansion, etc.
For example, whenever a user types a query “machine learning course”, the system shows the ML course on educative.io. -
User-based targeting: The ads will be subjective to the user based on a specific region, demographic, gender, age, etc.
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Interest-based targeting: This method shows interest-based ads. Assume that on Facebook, the advertiser might want to show ads based on certain interest hierarchies. For example, the advertiser might like to show sports-related ads to people interested in sports.
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Set-based targeting: This type shows ads to a set of users selected by the advertisers. For example, showing an ad to people who were previous buyers or have spent more than ten minutes on the website. Here, we can expand our set and do seed audience expansion.
2. User flow
As the platform user ...