Design an end-to-end recommendation system
SalesforceSoftware Engineer · Technical Screen
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Design an end-to-end recommendation system evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Design a movie recommendation system end to end. Clarify objectives (e.g., CTR, watch time, diversity), key signals (explicit ratings, implicit interactions), cold-start strategy, and a first modeling approach such as matrix factorization/NMF for user–item decomposition. Outline feature engineering, training data creation, offline evaluation (MAP/NDCG) and online A/B testing, freshness/real-time updates, and how you would roll out and monitor the system.
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