Knew this one from a forum post so I wasn't starting from zero.
Start by clarifying requirements and defining a simple rule-based filter (city, price) to establish a baseline. Then abstract rules into composable strategies using a common interface, and discuss how to combine them (e.g., AND/OR) and scale with caching and async processing. Emphasize extensibility, performance, and trade-offs between simplicity and flexibility.
Pro tip: Show how you'd evolve the system incrementally: begin with a naive implementation, identify bottlenecks (e.g., N+1 queries, lack of personalization), and propose a strategy pattern with a rule engine. This demonstrates pragmatic thinking and avoids over-engineering upfront.
Ask questions to understand functional and non-functional requirements: What defines relevance? How many users/events? What's the email frequency? What latency is acceptable? This ensures you design for the right scale and constraints.
Propose a basic implementation: query events by city and price, then send emails. Discuss data model (events, users, preferences) and simple SQL queries. Highlight limitations like lack of personalization and scalability.
Introduce a Strategy interface (e.g., `EventFilter`) with implementations like `CityFilter`, `PriceFilter`. Explain how to compose them using AND/OR combinators. Discuss how to register and execute strategies dynamically.
Discuss optimizations: caching user preferences, precomputing event indexes, using message queues for email sending, and batch processing. Consider trade-offs between real-time and batch recommendations.
Mention how to incorporate user behavior (clicks, purchases) to rank events, and A/B testing for strategy effectiveness. Discuss monitoring and iterating on strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.