My first instinct was to just throw everything into a frequency map and sort, which is basically right, but I initially forgot to filter out sessions that don't contain the target at all.
Clarify the problem constraints and edge cases, then propose an efficient solution using a hash map to count co-viewed products only in sessions containing the target. After counting, sort the products by frequency descending and product ID ascending, and return the top k. Discuss time and space complexity and potential optimizations for large-scale data.
Pro tip: Demonstrate awareness of real-world scalability by mentioning how you would handle massive session data with distributed processing (e.g., MapReduce) and how to efficiently retrieve top k using a heap instead of full sorting.
Ask about input size, data types, memory limits, and whether sessions can contain duplicates. Confirm that only sessions with the target product are considered and that the target is excluded from results.
Iterate through each session; if it contains the target, increment a frequency map for every other product in that session. Use a hash map for O(1) average updates.
Use a min-heap of size k to efficiently find the top k products by frequency, with a custom comparator that breaks ties by smaller product ID first. Alternatively, sort all products if k is close to the number of unique products.
Discuss time complexity: O(N) to scan sessions plus O(M log k) for heap operations, where N is total items and M is unique co-viewed products. Space complexity O(M). Mention potential optimizations like early filtering or parallel processing.
Consider cases where no sessions contain the target, k is larger than the number of co-viewed products, or there are ties. Walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.