Mid-level interview questionAsked at LinkedIn · interview write-up
Design a short-video recommender end to end, starting with which labels to optimize: watch time, completion, shares, hides and so on.
Model answer
No single label works: raw watch time favours long videos, completion favours short ones. So predict several labels with a multi-task ranker: watch time normalized by duration, completion, like, share, follow, and hides or fast skips as negatives, then combine them with weights tuned against long-term retention in A/B tests.
The pipeline: two-tower retrieval over video embeddings, the ranker, then re-ranking for diversity and freshness. Model position bias separately so the ranker learns preference.
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