Start by clarifying requirements: scale, latency, location update frequency, and definition of 'nearby'. Then propose a two-stage architecture: a fast candidate retrieval layer using geospatial indexing (e.g., geohash, S2, or H3) to fetch nearby places, followed by a ranking model that incorporates user context, real-time signals, and business objectives. Finally, discuss trade-offs, offline/online evaluation, and A/B testing for continuous improvement.
Pro tip: Emphasize the need for a streaming pipeline (e.g., Kafka, Flink) to handle location updates and feature freshness, and discuss how you'd balance latency vs. accuracy by using approximate nearest neighbor search and caching. Also, mention the importance of handling cold-start and sparsity in location data.
Ask about scale (DAU, QPS), latency SLA, location update frequency, and what 'nearby' means (radius, travel time). Also clarify business goals (e.g., engagement, conversions) and privacy constraints.
Outline components: mobile client, API gateway, location ingestion service, geospatial index, candidate retrieval, ranking service, and feedback loop. Emphasize real-time processing and low-latency serving.
Explain how to use geospatial indexing (e.g., geohash, S2, H3) to quickly fetch nearby places. Then describe a ranking model (e.g., GBDT, DNN) that uses features like distance, popularity, user preferences, and real-time context.
Discuss streaming infrastructure (Kafka, Flink) for location updates, caching strategies, and sharding of geospatial data. Mention trade-offs between exact vs. approximate nearest neighbor search.
Cover offline metrics (e.g., recall@k, NDCG) and online A/B testing with metrics like CTR, dwell time, and conversion. Explain how to handle cold-start and continuously improve the model.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
User fatigue modeling was the part I found most interesting and also the part I was least prepared for.
Start by clarifying the product context and objectives, then outline a high-level architecture that separates candidate generation, ranking, and filtering. Emphasize the ML models and features used for each stage, and discuss how to balance engagement with user well-being through metrics and trade-offs.
Pro tip: Frame the problem as a multi-objective optimization: you're not just maximizing clicks, but also minimizing notification fatigue and respecting user preferences. Mention how you'd measure long-term satisfaction (e.g., retention, mute rates) alongside short-term engagement.
Ask questions to understand the product goals, user base, notification types, and constraints. Define success metrics such as CTR, DAU, user satisfaction, and notification opt-out rates.
Outline a pipeline: ingestion from multiple sources, candidate generation, ranking, filtering, and delivery. Consider real-time vs. batch processing and scalability.
Describe ML models for ranking (e.g., learning-to-rank) and filtering (e.g., classifiers for suppression). Discuss features like user history, notification content, source reliability, and contextual signals.
Explain how to balance competing objectives (e.g., engagement vs. fatigue) using techniques like weighted objectives, constraints, or multi-task learning. Discuss how to set thresholds for suppression.
Propose offline evaluation (e.g., AUC, NDCG) and online A/B testing. Include guardrail metrics and a plan for continuous model improvement and feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.