The interviewer swapped to this mid-session after realizing his original question duplicated round one.
Start by clarifying requirements and scale, then outline a high-level architecture that includes data collection, candidate generation, ranking, and serving. Dive into algorithmic details for candidate generation (e.g., friends-of-friends, graph embeddings) and ranking (e.g., ML models), and discuss trade-offs and scalability.
Pro tip: Emphasize the importance of evaluating the system with offline metrics (e.g., precision@k, recall) and online A/B tests, and discuss how to handle cold-start users by leveraging alternative signals like location or interests.
Ask questions to understand functional and non-functional requirements: e.g., what defines a 'friend'? How many users? What latency is acceptable? What are privacy constraints?
Sketch the main components: data ingestion (user actions, graph), offline processing (candidate generation, model training), online serving (ranking, API), and feedback loop.
Describe methods to generate a set of potential friends, such as friends-of-friends, graph traversal, community detection, or embeddings (e.g., node2vec). Discuss scalability and trade-offs.
Explain how to rank candidates using features (e.g., mutual friends, interactions, profile similarity) and machine learning models (e.g., logistic regression, GBDT, neural networks). Mention online/offline feature consistency.
Discuss metrics (offline: precision, recall, NDCG; online: CTR, engagement), A/B testing, and how to handle cold-start and feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.