Design local sports team recommendation system

Design a recommendation system that suggests local sports teams to users.

High-level requirements:

• Recommend sports teams that are relevant to a user based on their location and interests.
• Consider how the underlying data (teams, locations, user interactions) is produced and ingested.
• Define what user-related information you would use to make recommendations.
• Describe how you would rank candidate teams for each user.
• Explain how each component of your system would scale as the number of users, teams, and interactions grows.

Key aspects to cover:

1. Data generation and ingestion
   • What data sources exist? (e.g., team info, league schedules, user sign-ups, user interactions such as follows, clicks, favorites, watch history.)
   • How this data flows into your system (batch vs. streaming, ETL/ELT pipelines).
2. User and team modeling
   • What user attributes you need (e.g., location, favorite sports, engagement history, device info, time of day).
   • What team attributes you need (e.g., sport type, league, home location, popularity).
3. Recommendation pipeline
   • How to generate candidate teams for a user (e.g., filter by location, popularity, similarity to what they follow).
   • How to rank those candidates (features, signals, and ranking algorithm/model).
4. System architecture and scaling
   • High-level component diagram (API layer, services, data stores, offline and online subsystems, caches).
   • Storage choices (SQL/NoSQL, search index, feature store, data warehouse).
   • How to handle increasing traffic and data volume (sharding, caching, replication, async processing).
5. Other considerations
   • Latency and freshness of recommendations.
   • Handling cold-start for new users and new teams.
   • Metrics and monitoring (e.g., click-through rate, engagement, latency).

Provide a high-level design and justify your major choices and trade-offs.