My first instinct was to just throw a set at it and call it done.
Start by clarifying the problem scope and constraints, then propose a greedy algorithm that processes rows in order, using a hash set to track seen show IDs and selecting the first unseen show for each position. Discuss trade-offs like greedy vs. optimal, and how to handle edge cases such as insufficient unique shows.
Pro tip: Emphasize that the greedy approach is optimal for maximizing uniqueness per row and that a hash set gives O(1) lookups, but also mention fallback strategies like allowing duplicates or showing placeholders when unique shows run out.
Ask about the number of rows and columns, whether shows can be repeated across different screens, and if there's a limit on fetching unique shows. Confirm that the goal is to avoid duplicates only on the first screen.
Use a hash set to track show IDs already placed on the screen for O(1) membership checks. Maintain a list of candidate shows per row (e.g., from recommendations) and possibly a global pool of unique shows.
Process rows sequentially. For each row, iterate through its candidate shows and pick the first one not in the seen set, adding it to the row and the set. If no unique show is available, decide on a fallback (e.g., allow duplicate, show placeholder, or fetch more).
Discuss time complexity (O(N) where N is total candidates), space complexity (O(K) for seen set), and trade-offs between strict uniqueness and user experience (e.g., showing fewer items vs. duplicates).
Explain why greedy works for this use case, how you'd handle insufficient unique shows, and how the solution scales with large catalogs. Mention potential optimizations like pre-filtering or caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.