How to design a high-performance MySQL table structure to implement the food recommendation function?

How to design a high-performance MySQL table structure to implement the food recommendation function?

As people’s demand for food becomes higher and higher, the application of recommendation systems in the field of food is gradually increasing. Designing a high-performance MySQL table structure to implement the food recommendation function will play an important role in improving user experience and platform development. This article will introduce how to design such a table structure and provide specific code examples.

1. Requirements Analysis
Before designing a high-performance food recommendation system, it is first necessary to clarify the system requirements. Generally speaking, a food recommendation system needs to meet the following requirements:

1. High performance: It can quickly perform recommendation calculations in the presence of massive data, and it can also support high-concurrency requests.
2. Accuracy: The recommendation system must be able to recommend the food that best suits the user's taste based on the user's preferences and behavior.
3. Scalability: The user base of the food recommendation system may gradually expand, so it should be able to support a large number of users.
4. Real-time: Users’ tastes and preferences may change at any time, so the recommendation system must be able to recommend food that suits their current tastes to users in a timely manner.

2. Table design
Based on the above demand analysis, we can design the following table structures to support the function of the food recommendation system:

1. User table (user ): Save the user's basic information, including user ID, user name, gender, age, etc.

CREATE TABLE user (
user_id INT PRIMARY KEY AUTO_INCREMENT,
username VARCHAR(100) NOT NULL ,
gender ENUM('male', 'female') NOT NULL,
age INT NOT NULL
);

2. foodtable(food): Saves the basic information of food, including food ID, food name, food type, etc.

CREATE TABLE food (
food_id INT PRIMARY KEY AUTO_INCREMENT,
food_name VARCHAR(100) NOT NULL ,
food_type VARCHAR(100) NOT NULL
);

3. User-food rating table (user_food_rating): Saves the user’s rating information for food, including user ID, food ID, rating value.

CREATE TABLE user_food_rating (
user_id INT NOT NULL,
food_id INT NOT NULL,
rating FLOAT NOT NULL,
PRIMARY KEY (user_id, food_id),
FOREIGN KEY (user_id) REFERENCES user (user_id),
FOREIGN KEY (food_id) REFERENCES food (food_id)
);

4. User-food preference table (user_food_preference): Saves the user's preference information for food, including user ID, food ID, and preference level.

CREATE TABLE user_food_preference (
user_id INT NOT NULL,
food_id INT NOT NULL,
preference FLOAT NOT NULL,
PRIMARY KEY (user_id, food_id),
FOREIGN KEY (user_id) REFERENCES user (user_id),
FOREIGN KEY (food_id) REFERENCES food (food_id)
);

5. Food similarity table (food_similarity): Saves the similarity information between foods, including food ID1, food ID2, and similarity value.

CREATE TABLE food_similarity (
food_id1 INT NOT NULL,
food_id2 INT NOT NULL,
similarity FLOAT NOT NULL,
PRIMARY KEY (food_id1, food_id2),
FOREIGN KEY (food_id1) REFERENCES food (food_id),
FOREIGN KEY (food_id2) REFERENCES food (food_id)
);

3. Code example

1. Query the user’s recommended food list

SELECT f.food_name, f.food_type
FROM food f
INNER JOIN (
SELECT food_id, SUM(similarity * preference) AS score
FROM user_food_preference ufp
INNER JOIN food_similarity fs ON ufp.food_id = fs.food_id1
WHERE ufp.user_id = 1
GROUP BY food_id
) AS t ON f.food_id = t.food_id
ORDER BY score DESC
LIMIT 10;

2. Update the user's score for food

INSERT INTO user_food_rating (user_id, food_id, rating)
VALUES (1, 1001, 4.5)
ON DUPLICATE KEY UPDATE rating = 4.5;

The above code examples are for reference only , which may need to be modified according to specific circumstances in actual applications.

In summary, through reasonable MySQL table structure design and optimization, a high-performance food recommendation system can be realized. At the same time, combined with real-time updated strategies and accurate recommendation algorithms, it can provide users with food recommendations that best suit their tastes. Of course, in practical applications, other factors need to be considered, such as cache, search engines, data sharding, etc., to further improve the performance and accuracy of the system.

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