Design recommendation and weapon-ad detection systems

You are asked to design two end-to-end ML systems. ## 1) Personalized recommendation system Design a system that recommends items (e.g., videos/products/posts) to users in a large-scale consumer app. Your design should address: - **Product goal:** what you are optimizing for (e.g., engagement, watch time, conversions) and how you balance multiple objectives. - **Data:** user events, item metadata, candidate sources, freshness, cold start. - **Modeling approach:** candidate generation vs. ranking (and optional re-ranking), feature design, handling exploration. - **Serving:** latency budget, online features, approximate nearest neighbor (if used), caching, fallbacks. - **Training & evaluation:** offline metrics, online A/B testing metrics/guardrails, counterfactual concerns. - **Monitoring & iteration:** drift, bias/fairness, abuse and feedback loops. ## 2) Weapon ad detection / ad safety classifier Design an automated system that detects whether an ad (image/text/video/landing page) contains weapons or weapon-related content, and blocks or routes it for manual review. Your design should address: - **Input modalities:** text, image frames, video, OCR, landing-page text. - **Labels & taxonomy:** what counts as “weapon content,” policy nuances, borderline cases. - **Modeling approach:** multimodal models, thresholding, calibration, ensembling. - **Decisioning workflow:** automated block vs. allow vs. send to human review; appeal process. - **Metrics:** precision/recall tradeoffs, cost of false positives/negatives, SLA for review. - **Operations:** active learning, handling adversarial ads, monitoring, model updates. Assume millions of users/items for recommendations and high ad throughput (thousands to millions/day) for ad review. Clearly state any additional assumptions you need.