Design an ads system to improve CTR

Q: Design an ads system to improve CTR

This is a ML System Design interview question from Pinterest for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

Design an ML system to increase the click-through rate (CTR) of ads shown in the feed of an online social media platform.

Address the following:

Goal and metrics : What are the online and offline metrics? How do you guard against regressions (e.g., user experience, revenue, long-term engagement)?
Overall architecture : Sketch the end-to-end pipeline from candidate ad retrieval to ranking to serving.
Training data : What logs do you need, what is the learning target/label, and how do you construct positives/negatives?
Embeddings : How would you build user/ad embeddings? You may reference approaches like graph-based recommendation (e.g., neighbor sampling / GNN-style methods) and two-tower models.
Practical concerns : cold start, bias/leakage, delayed feedback, calibration, exploration vs exploitation, monitoring, and iteration cadence.

Design an ads system to improve CTR

Solution

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