The core idea isn't that hard once you see it: take the last K movies for each user, throw them into a set, sort the set to get a canonical key, then use a hashmap to bucket users by that key.
Clarify that 'last K movies' means the most recent K movies in each user's ordered history, and that the set is order-independent. Then propose a hash map keyed by a canonical representation of the last K movies (e.g., a sorted tuple or a frozenset) to group users, and analyze time and space complexity.
Pro tip: Mention that if K is large, hashing a sorted tuple can be expensive; consider using a frozenset or a commutative hash (like XOR of movie IDs) to avoid sorting, but be aware of collision risks. Also, discuss edge cases like users with fewer than K movies.
Confirm that 'last K movies' means the most recent K movies in the ordered list, and that the set is order-independent. Discuss handling users with fewer than K movies (e.g., skip them or use all available).
For each user, extract the last K movies and create a canonical representation of the set, such as a sorted tuple or a frozenset, to use as a hash map key.
Iterate through users, compute the key, and append the user ID to a list in a hash map where the key maps to a list of user IDs.
After processing all users, filter out groups with fewer than two users and return the remaining groups.
Time complexity: O(N * (K log K)) if sorting each user's last K movies, or O(N * K) if using a frozenset. Space complexity: O(N * K) for storing keys and groups.
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