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:
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Goal and metrics
: What are the online and offline metrics? How do you guard against regressions (e.g., user experience, revenue, long-term engagement)?
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Overall architecture
: Sketch the end-to-end pipeline from candidate ad retrieval to ranking to serving.
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Training data
: What logs do you need, what is the learning target/label, and how do you construct positives/negatives?
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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.
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Practical concerns
: cold start, bias/leakage, delayed feedback, calibration, exploration vs exploitation, monitoring, and iteration cadence.