The 60-second answer
Clarify user intent, geographic radius/travel-time constraints, freshness, personalization, and whether the task is discovery, search, or route-aware recommendation. Generate candidates from geo indexes/category retrieval and enrich with place quality, popularity, user history, context, opening hours, and traffic/travel-time features.
Build the answer in this order
Clarify user intent, geographic radius/travel-time constraints, freshness, personalization, and whether the task is discovery, search, or route-aware recommendation.
Generate candidates from geo indexes/category retrieval and enrich with place quality, popularity, user history, context, opening hours, and traffic/travel-time features.
Rank with a multi-objective model balancing relevance, distance, diversity, freshness, and business/safety rules.
Evaluate by geography/category and monitor stale place data, regional cold start, latency, and online visit/satisfaction signals.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
Senior-level signal
- Senior answers address geographic selection bias, sparse regions, and privacy boundaries for location history.
- Explain how traffic/ETA uncertainty propagates into recommendation quality and fallback behavior.
What the interviewer is really testing
Likely follow-up questions
Common weak-answer patterns
- Jumping to a model before defining the product contract.
- Listing components without bottlenecks, metrics, or failure handling.
- Ignoring data quality, serving latency, monitoring, and iteration.