Design a Location Recommendation System
Company: Meta
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
Design a machine learning system that recommends places to a user in a maps or local-discovery product.
A user opens the app and expects relevant nearby suggestions such as restaurants, cafes, attractions, or other points of interest. The system should use signals such as the user's current location, time of day, historical behavior, preferences, and place metadata to rank candidates.
Discuss:
- product goals and success metrics
- candidate generation and retrieval
- ranking features and model choices
- cold-start handling for new users and new places
- real-time serving architecture and latency constraints
- feedback loops, exploration vs. exploitation, and online learning considerations
- offline and online evaluation
- privacy, bias, and abuse resistance
Quick Answer: This question evaluates a candidate's ability to design end-to-end machine learning recommendation systems, covering competencies in candidate generation, ranking models, feature engineering, real-time serving, evaluation metrics, and considerations for privacy, bias, and abuse resistance.