This is basically a full-stack ML design question and the scope is massive.
Start by clarifying the product context (e.g., Maps vs. Facebook Local), user intent, and scale. Then outline a two-stage recommendation system: candidate generation from geospatial and user signals, followed by ranking with a multi-objective model. Finally, discuss evaluation, A/B testing, and trade-offs like latency vs. personalization.
Pro tip: Emphasize how you would handle cold-start users and places by leveraging contextual signals (time, location, device) and cross-product data, and discuss how you'd measure success beyond CTR (e.g., direction requests, visits).
Ask about the product surface (Maps, Facebook, Instagram), user intent (search vs. browse), scale (DAU, QPS), latency requirements, and available data (user history, place attributes, social graph).
Propose methods to retrieve a few hundred candidates: geospatial indexing (e.g., geohash, S2), collaborative filtering, content-based similarity, and trending/popular places. Consider using embeddings for users and places.
Outline a multi-stage ranking system: a lightweight model for initial ranking, then a heavier model (e.g., DNN with embeddings) for final ranking. Incorporate features like user-place affinity, distance, category, time, and social signals.
Specify business and engagement metrics (CTR, direction requests, check-ins, dwell time). Discuss offline evaluation (NDCG, recall@k) and online A/B testing with guardrail metrics (latency, user satisfaction).
Discuss trade-offs: personalization vs. diversity, freshness vs. relevance, latency vs. model complexity. Propose an iteration plan: start with heuristics, then ML, then deep learning, with continuous experimentation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.