I started with candidate generation and ranking, which felt right, but I spent way too long on the retrieval layer and basically ran out of time before getting to re-ranking and diversity logic.
Start by clarifying the scope and requirements, then outline a high-level system design covering data sources, candidate generation, ranking, and serving. Dive into the ML components, including feature engineering, model selection, and evaluation metrics, while discussing trade-offs and scalability.
Pro tip: Emphasize the importance of real-time signals and user feedback loops, as YouTube's recommendations heavily rely on immediate user interactions to adapt and improve. Also, discuss how you would handle cold-start and diversity to avoid filter bubbles.
Ask questions to understand the scale, objectives (e.g., watch time, engagement), constraints, and available data. Define success metrics and consider both user and business perspectives.
Outline the main components: data collection (user interactions, video metadata), candidate generation (e.g., collaborative filtering, content-based), ranking (ML models), and serving (low-latency API). Mention offline vs. online processing.
Detail feature engineering (user, video, context features), model choices (e.g., matrix factorization, deep neural networks, two-tower models), training (batch vs. online), and evaluation (offline metrics like AUC, online A/B tests).
Discuss how to handle large-scale data and low-latency serving (e.g., distributed training, caching, approximate nearest neighbors). Balance trade-offs like relevance vs. diversity, freshness vs. popularity, and computational cost.
Explain how to monitor model performance, detect drift, and incorporate user feedback loops. Mention continuous experimentation and improvement strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.