MySQL implements the dish recommendation function of the ordering system

MySQL implements the dish recommendation function of the ordering system

In the catering industry, in order to provide a better user experience and increase sales, many restaurants will order food A dish recommendation function has been added to the system. This article will introduce how to use MySQL to implement the dish recommendation function of the ordering system and provide specific code examples.

1. Database design

In MySQL, we need to design two tables to implement the dish recommendation function. The first table is the dish table, which is used to store dish information, including dish ID, dish name, dish type and other fields. The second table is a user behavior table, which is used to store users' behaviors such as liking and collecting dishes, as well as some attributes related to the dishes, such as user ID, dish ID, behavior type and other fields.

The specific database table design is as follows:

Dish table (dish):
Field name type description
id int dish ID
name varchar dish name
type varchar dish type
...

User action table (user_action):
Field name type description
id int action ID
user_id int user ID
dish_id int dish ID
action_type int Action type (1 means like, 2 means favorite, etc.)
...

2. Data insertion and query

In practical applications, the menu table and The data in the user behavior table is dynamically inserted. You can insert the data into the table by writing corresponding insert statements, for example:

1. INSERT INTO dish (name, type) VALUES ('Kung Pao Chicken', 'Sichuan cuisine');
   INSERT INTO dish (name, type) VALUES ('Braised pork', 'Hunan cuisine');
   INSERT INTO dish (name, type) VALUES ( 'Fish-flavored shredded pork', 'Sichuan cuisine');
   ...
   
Insert user behavior data
2. INSERT INTO user_action (user_id, dish_id, action_type) VALUES (1, 1, 1); -- User 1 liked dish 1
   INSERT INTO user_action (user_id, dish_id, action_type) VALUES (1, 2, 2); -- User 1 liked dish 2
   INSERT INTO user_action (user_id, dish_id, action_type) VALUES (2, 1, 1); -- User 2 liked dish 1
   ...
   

After inserting the data, we can implement it by executing the relevant query statement Dishes recommendation function. For example, to query the favorite dishes of users with the same behavior, you can use the following SQL statement:

SELECT dish_id, COUNT(*) AS num

FROM user_action
WHERE action_type = 2 -- Favorite behavior
GROUP BY dish_id
ORDER BY num DESC
LIMIT 5; -- Return the top 5 dishes with the most number of collected dishes

3. Dish recommendation algorithm

Dish recommendation The function is not limited to recommendation based on user behavior, but can also use some recommendation algorithms, such as collaborative filtering algorithm, content filtering algorithm, etc. Here we take the collaborative filtering algorithm as an example to introduce a dish recommendation algorithm based on user behavior.

Calculate user interest matrix
1. By traversing the user behavior table and counting the number of user actions on dishes, a user interest matrix is ​​obtained.
   
Calculate the dish similarity matrix
2. By traversing the user behavior table, calculate the similarity between different dishes, and obtain a dish similarity matrix.
   
Recommendation based on similarity matrix
3. For each user, calculate a recommended dish list based on its interest and dish similarity matrix.
   

For specific code examples, please refer to the following link:

https://github.com/example/mysql-dish-recommendation

4. Summary

This article introduces how to use MySQL to implement the dish recommendation function of the ordering system, and provides specific code examples. By properly designing database tables, inserting data and executing query statements, recommendation functions based on user behavior can be achieved. At the same time, we also introduced a recommendation algorithm based on collaborative filtering algorithm to provide restaurants with more personalized and accurate dish recommendation services.

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