The product framing helped me actually think straight.
Clarify the problem constraints first, especially what 'visible portion' means and whether shelves can be reordered. Then propose an efficient algorithm using a hash set to track seen titles while iterating through shelves, and discuss trade-offs between time/space complexity and practical considerations like streaming data.
Pro tip: Demonstrate awareness of Netflix's scale by mentioning how this could be implemented in a distributed system or with streaming data, and discuss the importance of preserving shelf order and user experience.
Ask questions to understand the exact meaning of 'visible portion', viewport width X, and whether shelves can be modified or reordered. Confirm input/output formats and edge cases.
Propose using a hash set to track seen titles. Iterate through each shelf, and for each title, if it's not in the set, keep it and add to the set; otherwise, remove it or mark it as duplicate.
Discuss time complexity O(N) where N is total titles, and space O(U) where U is unique titles. Consider if in-place modification is allowed and memory constraints.
Address cases like empty shelves, all duplicates, and large datasets. Mention how to adapt for streaming or distributed processing if needed.
Walk through a small example to verify correctness, and discuss potential unit tests and performance testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.