The co-occurrence map part was fine, just iterate through orders and for each order containing the query product, increment counts for the other items.
Use a hash map to count co-purchase frequencies for the query product across all orders, then sort the results by frequency descending and product ID ascending. Clarify assumptions about input format and edge cases before coding.
Pro tip: Proactively discuss trade-offs between sorting all co-purchased products versus using a heap for top-k, and mention how to handle large-scale data with distributed counting if needed.
Ask about input format, whether orders can contain duplicate product IDs, and if the query product may not appear. Confirm output should be a list of product IDs.
Use a hash map to store co-purchase counts for each product that appears with the query product. Iterate through each order and update counts for all other products if the query product is present.
For each order containing the query product, iterate through the other products and increment their count in the hash map. Ensure each product is counted only once per order to avoid overcounting duplicates.
Convert the hash map entries to a list and sort by frequency descending, then by product ID ascending. Use a custom comparator or sort key.
Discuss time and space complexity. Consider optimizations like early termination if only top-k results are needed, or using a min-heap for large datasets.
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