← Microsoft Interview Insights
I jumped straight into a heap-based solution before nailing down the aggregation rule, which was a mistake.
Start by clarifying the scoring model: how to combine multiple keyword scores (e.g., sum, weighted sum, or max) and whether to normalize by app or keyword. Then propose an efficient algorithm using a min-heap of size K to find the top K apps without sorting all 500, and discuss trade-offs like precomputation or indexing for repeated queries.
Pro tip: Mention that with only 500 apps, a simple linear scan with a min-heap is optimal, but if the query volume is high, precomputing an inverted index from keywords to apps can reduce per-query work. Also, clarify tie-breaking rules upfront to avoid ambiguity.
Ask how to aggregate relevance scores for multiple keywords (e.g., sum, average, max) and whether scores are normalized. Confirm tie-breaking rules and if K can exceed the number of matching apps.
Use a hash map to store each app's keyword-to-score mapping for O(1) lookup. For top-K selection, use a min-heap of size K to keep the K highest scores efficiently.
For each app, compute its aggregate score for the query keywords. If the heap has fewer than K elements, push the app; otherwise, if the score exceeds the heap's minimum, replace it. Finally, extract and sort the heap to return results in descending order.
Time complexity: O(N log K) where N is the number of apps (500). Space: O(N + K). Discuss alternatives like sorting all scores (O(N log N)) or using a max-heap if K is large, and mention precomputing an inverted index for faster repeated queries.
Address cases where no apps match, K > number of matches, or ties. Suggest optimizations like early termination if scores are bounded, or caching frequent queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.