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This question sprawls in every direction and that's kind of the point.
Start by clarifying requirements and constraints, then walk through the high-level architecture covering data ingestion, user/team modeling, ranking, and scaling. Emphasize trade-offs and justify design choices with respect to Microsoft's scale and ecosystem.
Pro tip: Show awareness of cold-start and feedback loops by proposing hybrid approaches (content-based + collaborative filtering) and online learning to adapt to user behavior. Also, mention leveraging Azure services (e.g., Event Hubs, Cosmos DB, Machine Learning) to align with Microsoft's stack.
Ask about scale (users, teams), data sources, real-time vs batch, and success metrics. Define functional and non-functional requirements.
Outline how to collect user interactions (clicks, follows, views) and team data (schedules, stats, location) from various sources. Discuss batch and stream processing.
Describe feature engineering for users (demographics, location, preferences) and teams (sport, league, popularity, local relevance). Explain how to create embeddings and handle cold-start.
Explain candidate generation (e.g., collaborative filtering, content-based) and ranking (e.g., learning-to-rank). Discuss blending multiple signals and personalization.
Describe how to scale each component (e.g., sharding, caching, distributed processing) and ensure low-latency serving. Mention monitoring, A/B testing, and feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.