How do you solve Find Product Recommendation Pairs on LeetCode?

Find Product Recommendation Pairs (LeetCode #3521) 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: Co-purchase patterns reveal user behavior.

What is the time complexity of Find Product Recommendation Pairs?

The optimal solution for Find Product Recommendation Pairs runs in O(n) time and uses O(n) space. This approach efficiently counts pairs using a hash map, reducing the need for nested loops.

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

The brute force approach for Find Product Recommendation Pairs is Brute Force, with O(n²) time complexity and O(1) space complexity. Check every pair of products purchased by each customer to find co-purchases. This method is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

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

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

What are the interview tips for Find Product Recommendation Pairs?

Explain your thought process clearly. Discuss trade-offs between brute force and optimal solutions. Practice SQL queries for data manipulation.

What are common mistakes when solving Find Product Recommendation Pairs?

Not considering product order (product1 < product2). Failing to count distinct users.

#3521

Find Product Recommendation Pairs

Medium

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)

Utilize a hash map to count co-purchases efficiently, reducing redundant checks and improving performance.

⚙️

Algorithm

3 steps

1Step 1: Create a hash map to store product pairs and their customer counts.
2Step 2: For each user, retrieve their purchased products and generate pairs, updating the hash map.
3Step 3: Filter the hash map for pairs with at least 3 customers.

solution.py7 lines

1# Full working Python code
2SELECT product1_id, product2_id, customer_count
3FROM (SELECT pp1.product_id AS product1_id, pp2.product_id AS product2_id, COUNT(DISTINCT pp.user_id) AS customer_count
4FROM ProductPurchases pp
5JOIN ProductPurchases pp2 ON pp.user_id = pp2.user_id AND pp.product_id < pp2.product_id
6GROUP BY pp1.product_id, pp2.product_id) AS pairs
7WHERE customer_count >= 3;

ℹ

Complexity note: This approach efficiently counts pairs using a hash map, reducing the need for nested loops.

1Co-purchase patterns reveal user behavior.
2Using hash maps optimizes counting pairs.

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