I went straight to set intersection and it felt clean enough.
Start by clarifying the problem and constraints, then propose a two-phase algorithm: first compute mutual friend counts for all non-friend candidates, then select the top K using a min-heap. Walk through the implementation using the User class, and analyze time and memory complexity, discussing trade-offs and potential optimizations.
Pro tip: Mention that for large-scale systems, you can avoid scanning all non-friends by using a threshold or sampling, and highlight the importance of tie-breaking and excluding existing friends.
Ask about the size of the network, whether the graph is directed or undirected, and if K is small relative to the number of users. Confirm that the User class has a friends list.
For a given user u, iterate over each friend f of u, then for each friend g of f that is not u and not already a friend of u, increment a counter for g. Use a hash map to store counts.
After counting, use a min-heap of size K to find the K candidates with the highest mutual friend counts. Iterate through the counts and maintain the heap.
Assume User has an id and a list of friends (User objects). Write pseudocode or actual code for the algorithm, handling edge cases like no friends or fewer than K candidates.
Time complexity: O(F * avg_friends) for counting, plus O(N log K) for heap selection, where F is number of friends and N is number of candidates. Memory: O(N) for the hash map. Discuss alternatives like sorting all candidates (O(N log N)) or using approximate methods for scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Genuinely surprised they let you use an AI tool mid-interview.
Start by clarifying the ranking problem and the role of mutual friends as a baseline signal. Then propose alternative graph-based signals like Adamic-Adar and Jaccard similarity, explaining their computation and intuition. Finally, compare them on trade-offs such as computational cost, sensitivity to node degree, and performance in different network contexts, and suggest how to evaluate them offline and online.
Pro tip: Show that you understand the production constraints at Meta's scale: Adamic-Adar is more expensive but often better for sparse graphs, while Jaccard is cheaper but biased toward high-degree nodes. Mention that the best signal may depend on the specific use case (e.g., friend recommendations vs. group suggestions).
Restate the ranking task (e.g., recommending friends or connections) and explain why mutual friends is a common baseline signal. Mention its limitations, such as popularity bias and lack of nuance.
Introduce Adamic-Adar and Jaccard similarity as alternatives. Define each: Adamic-Adar weights common neighbors by inverse log degree, while Jaccard normalizes by the union of neighbors.
Compare the signals on computational complexity, sensitivity to node degree, and effectiveness in different network densities. Discuss how Adamic-Adar penalizes high-degree common neighbors, reducing popularity bias, while Jaccard is simpler and faster but may over-penalize high-degree nodes.
Explain how to evaluate these signals offline (e.g., AUC, precision@k) and online (A/B tests). Discuss scalability and implementation challenges at Meta's scale, such as approximate algorithms or precomputation.
Suggest a recommendation based on the trade-offs, possibly combining signals or using a learned model. Emphasize the importance of experimentation and iteration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.