I started with the similarity logic and it felt clean enough, group ratings by user, then for each movie the target user rated highly, find others who also rated it highly.
First, clarify the input format and edge cases, then design an efficient algorithm using hash maps to group ratings by user and movie. Identify similar users based on shared high ratings, collect their high-rated movies not rated by the target user, and deduplicate the results. Discuss time/space complexity and potential optimizations.
Pro tip: Demonstrate awareness of real-world data sparsity and scalability by mentioning how you'd handle large datasets (e.g., using distributed processing or approximate similarity) and the importance of defining 'similar' precisely to avoid false positives.
Ask about input size, rating scale, and whether the target user is included in the list. Confirm that similarity requires both users to have rated the same movie 4 or 5, and that recommendations exclude movies the target user has already rated.
Use a hash map to store each user's ratings (movie -> rating) and another to store each movie's raters (movie -> set of users who rated it 4 or 5). This enables efficient lookup of similar users and their high-rated movies.
For the target user, iterate over their high-rated movies (4 or 5) and for each, find other users who also rated that movie 4 or 5. Collect these users as similar.
For each similar user, gather movies they rated 4 or 5 that the target user hasn't rated. Use a set to avoid duplicates, then convert to a list for output.
Discuss time and space complexity (e.g., O(N) where N is number of ratings) and potential improvements like early termination or using approximate similarity for large-scale systems.
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