This is the bread and butter of ML design rounds at big social platforms, so I wasn't surprised.
Start by clarifying requirements and scale, then walk through the end-to-end ML pipeline from candidate generation to ranking and serving, emphasizing trade-offs at each stage. Highlight how you would measure success and iterate using online metrics and A/B testing.
Pro tip: Anchor your design around the user's short-term and long-term satisfaction, not just click-through rate, and mention how you'd balance exploration and exploitation to avoid feedback loops.
Ask about scale (DAU, videos), latency constraints, and business goals (engagement, retention). Define success metrics like watch time, completion rate, and user satisfaction.
Outline the main components: candidate generation (retrieval), ranking, re-ranking, and serving. Mention data pipelines for training and feature engineering.
Describe multiple retrieval sources (e.g., collaborative filtering, content-based, trending, social graph) and how to combine them efficiently using ANN or inverted indices.
Explain the ranking model (e.g., deep neural network with multi-task learning) and re-ranking for diversity, freshness, and business rules. Discuss feature engineering and model training.
Cover low-latency serving, online A/B testing, and offline evaluation. Discuss how to monitor and mitigate feedback loops and biases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining the core components of a nearby-place recommendation system: data sources (user location, place attributes, user preferences), candidate generation (spatial indexing), ranking (personalization and context), and evaluation. Then contrast it with standard content recommendation, emphasizing the unique challenges of geospatial data, real-time constraints, and the importance of distance and freshness.
Pro tip: Highlight the trade-off between exploration and exploitation in a spatial context: users may be willing to travel farther for highly relevant places, but distance decay must be modeled. Also, mention the cold-start problem for new places and how to leverage geographic features.
Ask clarifying questions about scale (number of users, places), latency requirements, data availability (user history, place metadata), and business goals (e.g., increase engagement, drive foot traffic).
Identify key data: user location (GPS, check-ins), place attributes (category, ratings, popularity), user preferences (historical interactions), and contextual signals (time of day, weather). Discuss geospatial feature engineering like distance, density, and neighborhood embeddings.
Outline a two-stage architecture: candidate generation using spatial indexing (e.g., geohash, Quadtree) to retrieve nearby places, followed by ranking with a machine learning model that incorporates distance, personalization, and context.
Explain how to model distance decay (e.g., exponential decay), incorporate user-item interactions, and handle cold-start for new places. Discuss model choices (e.g., GBDT, neural networks) and how to combine features.
Define offline metrics (e.g., recall@k, NDCG) and online metrics (CTR, conversion, user engagement). Discuss A/B testing, handling position bias, and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Harder than it sounds because it's not just ranking, it's also a multi-objective problem with fatigue and unsubscription risk baked in.
Start by framing the problem as a ranking and optimization challenge with multiple objectives: user engagement, satisfaction, and business value. Propose a machine learning system that scores each notification based on predicted user interaction and utility, then applies constraints like frequency capping and timing optimization. Emphasize the need for a multi-objective approach that balances short-term metrics with long-term user retention.
Pro tip: Highlight the importance of negative feedback signals (e.g., dismissals, opt-outs) and long-term user satisfaction metrics, not just click-through rates, to avoid optimizing for short-term engagement at the expense of user trust.
Clarify the goals: maximize user engagement (e.g., clicks, comments), user satisfaction (e.g., reduced notification fatigue), and business value (e.g., ad revenue). Define metrics like CTR, conversion rate, dismissal rate, and long-term retention.
For each notification, compute features: user-notification affinity (historical interactions), content type, recency, urgency, and contextual factors (time of day, device). Train a model to predict the probability of positive user action (e.g., click) and negative action (e.g., dismiss).
Combine scores into a utility function that balances predicted engagement and user value. Rank notifications by utility, then select top ones subject to constraints like frequency caps, diversity, and timing rules.
Determine the optimal time to send each notification using models that predict user responsiveness by time of day, day of week, and user activity patterns. Consider batching to avoid overwhelming the user.
Evaluate the system via online A/B tests measuring both short-term engagement and long-term user satisfaction (e.g., retention, notification opt-out rates). Continuously refine models with feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.