Seemed straightforward until I started thinking about whether to sort every time or maintain a heap.
Clarify the requirements: whether scores update dynamically and if get_top_k is called frequently. Then propose a data structure that supports efficient score updates and top-k queries, such as a hash map for scores combined with a heap or balanced BST. Discuss trade-offs between different approaches and analyze time/space complexity.
Pro tip: Mention that if get_top_k is called frequently, you can maintain a sorted structure or cache the top-k results, but be prepared to discuss the trade-off between update and query performance. Also, consider using a min-heap of size k for O(n log k) top-k selection when k is small.
Ask whether scores are updated in real-time and how often get_top_k is called. Determine if k is fixed or variable, and if ties need special handling.
Propose using a hash map to store article scores for O(1) updates. For top-k queries, consider a heap, balanced BST, or sorting depending on frequency.
For each get_top_k call, if using a heap, iterate through all articles and maintain a min-heap of size k. Alternatively, maintain a sorted list or use a quickselect algorithm.
Discuss time and space complexity: O(1) update, O(n log k) for heap-based top-k, or O(n) average with quickselect. Compare with maintaining sorted order O(log n) update and O(k) query.
Consider k larger than number of articles, negative scores, ties, and concurrency if updates and queries happen simultaneously.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.