The part I fumbled a bit was the PriceService wrinkle.
Start by clarifying requirements and scale, then define core data models (User, Event, Recommendation) and a RecommendationStrategy interface with pluggable implementations (CityBased, PriceBased). Explain how a RecommendationEngine orchestrates strategies, uses PriceService for pricing, and how to keep the system extensible via dependency injection and configuration.
Pro tip: Emphasize that strategies should be independently testable and that the engine should support fallback and ranking to handle cases where no single strategy yields results. Also mention monitoring strategy performance to inform future improvements.
Ask about scale (users, events), latency, personalization depth, and integration points. Confirm that the initial focus is on city and price rules with a pluggable architecture.
Outline core entities: User (id, city, price sensitivity), Event (id, city, price, date, category), and Recommendation (user, event, score, strategy). Consider relationships and indexes for efficient querying.
Define RecommendationStrategy interface with a method like recommend(user, events) returning scored events. Define PriceService interface with methods like getPrice(eventId) and getPriceRange(eventId). Show how CityBasedStrategy and PriceBasedStrategy implement RecommendationStrategy.
Describe how the engine accepts a list of strategies, executes them (possibly in parallel), aggregates and ranks results, and applies fallbacks. Explain how PriceService is injected into strategies that need it.
Discuss adding new strategies without modifying existing code (Open/Closed Principle), using configuration to enable/disable strategies, and scaling via caching, async processing, and monitoring.
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