My first pass totally ignored the reuse case.
Clarify the requirements and edge cases first, then outline a greedy randomized algorithm that filters eligible ads, shuffles them, and assigns while tracking counts. Discuss time/space complexity and potential optimizations like weighted random selection or handling large-scale systems.
Pro tip: Mention that in production systems like eBay's, ad assignment often involves additional constraints (e.g., pacing, targeting) and randomization may be weighted by bid or relevance; showing awareness of these real-world factors demonstrates maturity.
Ask about input/output formats, definition of 'eligible' (e.g., remaining limit > 0), behavior when no eligible ads exist, and whether positions can be left unassigned. Confirm if duplicate avoidance is strict or best-effort.
Propose a greedy approach: filter ads with remaining limit > 0, shuffle them, then iterate over positions assigning ads while avoiding duplicates if possible. If duplicates are unavoidable, reuse ads with remaining limit > 0.
Increment the display count for each assigned ad and update the user's state. Return both the assignment map (position -> ad) and the updated counts.
State time complexity O(P * A) where P is positions and A is ads, and space O(P + A). Discuss potential improvements like using a priority queue for weighted random selection or pre-filtering ads.
Walk through a simple example, then test edge cases: no eligible ads, more positions than eligible ads, and ads with zero remaining limit. Ensure the solution handles these gracefully.
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