This is basically four questions in one and I didn't realize that until I was already three minutes into just describing the product.
Start by framing the problem: define the user value and business goals for notification recommendations in a news app. Then walk through the end-to-end system: data signals, ML models, ranking, and delivery. Finally, describe how you would test and iterate using A/B tests and guardrail metrics.
Pro tip: Emphasize the balance between engagement and user trust—over-notifying can lead to uninstalls, so include metrics like notification dismissal rate and opt-out rate as guardrails.
Define the objective: increase user engagement (e.g., click-through, daily active users) while maintaining user trust. Clarify constraints like platform (iOS/Android), existing infrastructure, and privacy policies.
Explain how users interact with notifications: they receive personalized news alerts based on interests, reading history, and real-time trends. Notifications can be breaking news, personalized digests, or recommended reads.
Detail the data pipeline: user events (clicks, reads, dismissals) feed into a feature store; ML models (e.g., collaborative filtering, content-based) score candidate articles; a ranking system orders them; and a delivery service sends notifications via push infrastructure.
Describe how the system decides which notifications to send: it considers user preferences, real-time context (time of day, location), article freshness, and predicted engagement. It also applies business rules (e.g., max notifications per day) and deduplication.
Propose A/B tests to measure impact on key metrics (CTR, retention) and guardrails (opt-out rate, dismissal rate). Include offline evaluation (precision@k) and online experiments with holdout groups. Plan for iterative improvements based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.