How do you solve Find Category Recommendation Pairs on LeetCode?

Find Category Recommendation Pairs (LeetCode #3554) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Utilizing hash maps can significantly reduce time complexity.

What is the time complexity of Find Category Recommendation Pairs?

The optimal solution for Find Category Recommendation Pairs runs in O(n) time and uses O(n) space. The complexity is linear since we only iterate through the data a few times and use hash maps for efficient lookups.

What is the brute force solution for Find Category Recommendation Pairs?

The brute force approach for Find Category Recommendation Pairs is Brute Force, with O(n²) time complexity and O(1) space complexity. We can check every possible pair of categories and count unique users for each pair. This is straightforward but inefficient as it involves nested loops. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Find Category Recommendation Pairs?

Find Category Recommendation Pairs uses the following concepts: Database. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Find Category Recommendation Pairs?

Clarify requirements before coding. Discuss your thought process aloud. Optimize after implementing a working solution.

What are common mistakes when solving Find Category Recommendation Pairs?

Overlooking the need for unique user counts. Not considering category ordering in pairs.

#3554

Find Category Recommendation Pairs

Hard

DatabaseHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

We can use a hash map to group users by category. This allows us to efficiently find common users between category pairs without nested loops.

⚙️

Algorithm

3 steps

1Step 1: Create a map where each category maps to a set of users who purchased products in that category.
2Step 2: For each category pair, find the intersection of user sets and count unique users.
3Step 3: Report pairs with at least 3 unique users.

solution.py9 lines

1# Full working Python code
2SELECT category1, category2
3FROM (SELECT c1.category AS category1, c2.category AS category2, COUNT(DISTINCT pp.user_id) AS user_count
4FROM ProductInfo c1
5JOIN ProductPurchases pp ON c1.product_id = pp.product_id
6JOIN ProductInfo c2 ON c1.category < c2.category
7JOIN ProductPurchases pp2 ON c2.product_id = pp2.product_id AND pp.user_id = pp2.user_id
8GROUP BY category1, category2) AS counts
9WHERE user_count >= 3;

ℹ

Complexity note: The complexity is linear since we only iterate through the data a few times and use hash maps for efficient lookups.

1Utilizing hash maps can significantly reduce time complexity.
2Understanding user-category relationships is crucial for efficient querying.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.