Design feedback-driven recommender
GoogleMachine Learning Engineer · Onsite
Last updated
Design feedback-driven recommender 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.
Question
Design an online learning or bandit-style recommendation system: at each interaction you must choose 1 item out of 4 candidates to show the user, receive immediate feedback, and update the model so that future selections improve over time. Detail model choice, feature engineering, feedback handling, exploration–exploitation strategy, and offline/online evaluation.
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