## Scenario You are designing a **short-video recommendation system** (similar to a swipe/feed product). The system must personalize the feed for each user and **react quickly to the user’s short-term interests** (session intent), not just long-term preferences. ## Requirements 1. **Primary goal:** maximize user satisfaction in the current session by capturing short-term interest shifts (e.g., user starts watching cooking videos after browsing sports). 2. **Scale assumptions (choose reasonable numbers and state them):** tens of millions of users, millions of videos, high QPS during peak. 3. **Latency:** feed generation should feel instant (e.g., p95 < 200–300 ms for ranking at request time). 4. **Freshness:** incorporate new uploads and the user’s latest interactions quickly. 5. **Safety & quality:** filter harmful/low-quality/spam content; avoid repetitive or overly narrow recommendations. 6. **Feedback signals:** watch time, completion rate, likes, shares, comments, follows, “not interested,” skips, and dwell time. ## Deliverables - Propose an end-to-end architecture for candidate generation + ranking. - Explain **how you model and serve short-term interest** (session-based signals, near-real-time features, or online learning). - Define core offline/online metrics and experimentation approach. - Discuss data pipelines, feature stores, model training, and serving. - Call out key pitfalls (feedback loops, cold start, bias, exploration vs exploitation).