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| 1 | +[](https://github.com/javadev/LeetCode-in-Kotlin) |
| 2 | +[](https://github.com/javadev/LeetCode-in-Kotlin/fork) |
| 3 | + |
| 4 | +## 3554\. Find Category Recommendation Pairs |
| 5 | + |
| 6 | +Table: `ProductPurchases` |
| 7 | + |
| 8 | + +-------------+------+ |
| 9 | + | Column Name | Type | |
| 10 | + +-------------+------+ |
| 11 | + | user_id | int | |
| 12 | + | product_id | int | |
| 13 | + | quantity | int | |
| 14 | + +-------------+------+ |
| 15 | + (user_id, product_id) is the unique identifier for this table. |
| 16 | + Each row represents a purchase of a product by a user in a specific quantity. |
| 17 | + |
| 18 | +Table: `ProductInfo` |
| 19 | + |
| 20 | + +-------------+---------+ |
| 21 | + | Column Name | Type | |
| 22 | + +-------------+---------+ |
| 23 | + | product_id | int | |
| 24 | + | category | varchar | |
| 25 | + | price | decimal | |
| 26 | + +-------------+---------+ |
| 27 | + product_id is the unique identifier for this table. |
| 28 | + Each row assigns a category and price to a product. |
| 29 | + |
| 30 | +Amazon wants to understand shopping patterns across product categories. Write a solution to: |
| 31 | + |
| 32 | +1. Find all **category pairs** (where `category1` < `category2`) |
| 33 | +2. For **each category pair**, determine the number of **unique** **customers** who purchased products from **both** categories |
| 34 | + |
| 35 | +A category pair is considered **reportable** if at least `3` different customers have purchased products from both categories. |
| 36 | + |
| 37 | +Return _the result table of reportable category pairs ordered by **customer\_count** in **descending** order, and in case of a tie, by **category1** in **ascending** order lexicographically, and then by **category2** in **ascending** order._ |
| 38 | + |
| 39 | +The result format is in the following example. |
| 40 | + |
| 41 | +**Example:** |
| 42 | + |
| 43 | +**Input:** |
| 44 | + |
| 45 | +ProductPurchases table: |
| 46 | + |
| 47 | + +---------+------------+----------+ |
| 48 | + | user_id | product_id | quantity | |
| 49 | + +---------+------------+----------+ |
| 50 | + | 1 | 101 | 2 | |
| 51 | + | 1 | 102 | 1 | |
| 52 | + | 1 | 201 | 3 | |
| 53 | + | 1 | 301 | 1 | |
| 54 | + | 2 | 101 | 1 | |
| 55 | + | 2 | 102 | 2 | |
| 56 | + | 2 | 103 | 1 | |
| 57 | + | 2 | 201 | 5 | |
| 58 | + | 3 | 101 | 2 | |
| 59 | + | 3 | 103 | 1 | |
| 60 | + | 3 | 301 | 4 | |
| 61 | + | 3 | 401 | 2 | |
| 62 | + | 4 | 101 | 1 | |
| 63 | + | 4 | 201 | 3 | |
| 64 | + | 4 | 301 | 1 | |
| 65 | + | 4 | 401 | 2 | |
| 66 | + | 5 | 102 | 2 | |
| 67 | + | 5 | 103 | 1 | |
| 68 | + | 5 | 201 | 2 | |
| 69 | + | 5 | 202 | 3 | |
| 70 | + +---------+------------+----------+ |
| 71 | + |
| 72 | +ProductInfo table: |
| 73 | + |
| 74 | + +------------+-------------+-------+ |
| 75 | + | product_id | category | price | |
| 76 | + +------------+-------------+-------+ |
| 77 | + | 101 | Electronics | 100 | |
| 78 | + | 102 | Books | 20 | |
| 79 | + | 103 | Books | 35 | |
| 80 | + | 201 | Clothing | 45 | |
| 81 | + | 202 | Clothing | 60 | |
| 82 | + | 301 | Sports | 75 | |
| 83 | + | 401 | Kitchen | 50 | |
| 84 | + +------------+-------------+-------+ |
| 85 | + |
| 86 | +**Output:** |
| 87 | + |
| 88 | + +-------------+-------------+----------------+ |
| 89 | + | category1 | category2 | customer_count | |
| 90 | + +-------------+-------------+----------------+ |
| 91 | + | Books | Clothing | 3 | |
| 92 | + | Books | Electronics | 3 | |
| 93 | + | Clothing | Electronics | 3 | |
| 94 | + | Electronics | Sports | 3 | |
| 95 | + +-------------+-------------+----------------+ |
| 96 | + |
| 97 | +**Explanation:** |
| 98 | + |
| 99 | +* **Books-Clothing**: |
| 100 | + * User 1 purchased products from Books (102) and Clothing (201) |
| 101 | + * User 2 purchased products from Books (102, 103) and Clothing (201) |
| 102 | + * User 5 purchased products from Books (102, 103) and Clothing (201, 202) |
| 103 | + * Total: 3 customers purchased from both categories |
| 104 | +* **Books-Electronics**: |
| 105 | + * User 1 purchased products from Books (102) and Electronics (101) |
| 106 | + * User 2 purchased products from Books (102, 103) and Electronics (101) |
| 107 | + * User 3 purchased products from Books (103) and Electronics (101) |
| 108 | + * Total: 3 customers purchased from both categories |
| 109 | +* **Clothing-Electronics**: |
| 110 | + * User 1 purchased products from Clothing (201) and Electronics (101) |
| 111 | + * User 2 purchased products from Clothing (201) and Electronics (101) |
| 112 | + * User 4 purchased products from Clothing (201) and Electronics (101) |
| 113 | + * Total: 3 customers purchased from both categories |
| 114 | +* **Electronics-Sports**: |
| 115 | + * User 1 purchased products from Electronics (101) and Sports (301) |
| 116 | + * User 3 purchased products from Electronics (101) and Sports (301) |
| 117 | + * User 4 purchased products from Electronics (101) and Sports (301) |
| 118 | + * Total: 3 customers purchased from both categories |
| 119 | +* Other category pairs like Clothing-Sports (only 2 customers: Users 1 and 4) and Books-Kitchen (only 1 customer: User 3) have fewer than 3 shared customers and are not included in the result. |
| 120 | + |
| 121 | +The result is ordered by customer\_count in descending order. Since all pairs have the same customer\_count of 3, they are ordered by category1 (then category2) in ascending order. |
| 122 | + |
| 123 | +## Solution |
| 124 | + |
| 125 | +```sql |
| 126 | +# Write your MySQL query statement below |
| 127 | +SELECT |
| 128 | + pi1.category AS category1, |
| 129 | + pi2.category AS category2, |
| 130 | + COUNT(DISTINCT pp1.user_id) AS customer_count |
| 131 | +FROM |
| 132 | + ProductPurchases pp1, |
| 133 | + ProductPurchases pp2, |
| 134 | + ProductInfo pi1, |
| 135 | + ProductInfo pi2 |
| 136 | +WHERE |
| 137 | + pp1.user_id = pp2.user_id |
| 138 | + AND pi1.category < pi2.category |
| 139 | + AND pp1.product_id = pi1.product_id |
| 140 | + AND pp2.product_id = pi2.product_id |
| 141 | +GROUP BY |
| 142 | + pi1.category, |
| 143 | + pi2.category |
| 144 | +HAVING |
| 145 | + COUNT(DISTINCT pp1.user_id) >= 3 |
| 146 | +ORDER BY |
| 147 | + customer_count DESC, |
| 148 | + category1 ASC, |
| 149 | + category2 ASC; |
| 150 | +``` |
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