The core logic isn't hard once you see it: collect all product IDs from friends' purchases into a set, subtract the user's own purchases, then map the remaining IDs to product names.
Clarify the data structures and edge cases, then propose an efficient set-based solution. Build a set of the user's purchased product names, iterate through each friend's purchases, and collect product names not in the user's set. Return the resulting set or list, ensuring uniqueness.
Pro tip: Mention that using a hash set for the user's purchases gives O(1) lookups, making the overall solution O(total friend purchases) time, which is optimal. Also, discuss how you would handle duplicates and null/empty inputs to show production-level thinking.
Ask about input format, expected output (set vs list), handling of duplicates, null/empty friends or purchases, and whether product names are case-sensitive.
Propose using a hash set to store the user's purchased product names for O(1) membership checks. Then iterate through each friend's purchases and collect names not in the set.
State that the time complexity is O(P_user + P_friends) where P is the number of purchases, and space complexity is O(P_user + K) for the set and result, with K being the number of unique friend-only products.
Discuss handling of empty friends list, empty purchases, duplicate product names across friends, and null values. Ensure the result contains unique product names.
Implement the function with clear variable names and comments. Walk through a small example to verify correctness, including edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.