This is the kind of question where you can ramble forever if you're not careful.
Start by outlining the ideal metrics for a friend-recommendation system, such as precision, recall, and engagement, then systematically evaluate which can be computed with only user IDs and friend lists. Emphasize that without interaction data, many metrics are not computable, and discuss potential proxies or data augmentation strategies.
Pro tip: Acknowledge the data limitation upfront and propose how additional data (e.g., interactions, impressions) would enable richer metrics, showing you understand the full lifecycle of ML systems.
List metrics that would fully evaluate a friend-recommendation algorithm, such as precision@k, recall, NDCG, click-through rate, and user engagement.
For each metric, determine if it can be computed using only user IDs and current friend lists, noting which require additional data like interactions or impressions.
Select metrics that are computable, such as link prediction accuracy (e.g., AUC) using held-out edges, or graph-based metrics like common neighbors.
Suggest proxy metrics that approximate the ideal ones, such as friend request acceptance rate if available, or offline evaluation using historical friend additions.
Explain the limitations of the computable metrics and how additional data (e.g., interactions, impressions) would enable more comprehensive evaluation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The AI-allowed part sounds like a gift until you realize they're watching how you handle the output.
Start by clarifying the requirements: what data structures represent users and friendships, what defines a 'mutual friend', and how the score should be computed (e.g., count, Jaccard similarity). Then design an efficient algorithm using hash sets for O(min(|A|, |B|)) intersection, implement it with clean code, and write comprehensive unit tests covering edge cases, performance, and correctness. Explicitly discuss how you would use AI assistance responsibly: generate initial code, then manually review, test, and integrate.
Pro tip: Demonstrate ownership by not just accepting AI-generated code: walk through how you'd validate it with tests, check for edge cases like empty friend lists or duplicate friendships, and consider scalability for large graphs. Mention that you'd also discuss trade-offs between different scoring methods (e.g., raw count vs. normalized) with the interviewer.
Ask questions to define the input format (e.g., user objects with friend lists), the definition of mutual friends, and the desired scoring function (e.g., count, Jaccard index). Confirm edge cases like empty lists, self-friendship, and duplicate entries.
Choose efficient data structures: represent each user's friends as a hash set for O(1) lookups. Compute the intersection of the two sets to find mutual friends, then apply the scoring formula. Discuss time and space complexity.
Use AI to generate a first draft, but manually review for correctness, style, and edge cases. Ensure the code is clean, well-commented, and handles all identified edge cases. Integrate the AI output thoughtfully, not blindly.
Create tests covering normal cases, empty friend lists, no mutual friends, all mutual friends, duplicate friendships, and large inputs for performance. Use a testing framework like pytest or unittest, and include assertions for expected scores.
Run tests, debug any failures, and explain how you validated the AI-generated code. Discuss alternative scoring methods (e.g., cosine similarity, weighted edges) and their implications for ML applications like friend recommendation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.