Design an ads system to improve CTR

Q: Design an ads system to improve CTR

This question evaluates machine learning system design competency for improving ad click-through rate, testing understanding of metrics, end-to-end architecture, training data and embeddings, and operational issues like cold start, bias and delayed feedback within advertising and recommender domains.

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ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

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

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