How to design the recommended product table structure of the mall in MySQL?

How to design the recommended product table structure of the mall in MySQL?

With the rapid development of e-commerce, mall recommendation systems have become an important means to improve user shopping experience and increase sales. The structural design of the recommended product table in the mall is crucial to the effectiveness and efficiency of the recommendation system. This article will introduce how to design the recommended product table structure of the mall in MySQL and provide specific code examples.

1. Design Principles of Recommended Product Table
Before designing the structure of the recommended product table in the mall, we need to clarify some design principles to provide a better user experience and recommendation accuracy.

1. Flexibility of data structure: The structure of the recommended product table needs to be extensible to cope with different types of recommendation algorithms and data requirements.
2. Quick retrieval: The recommended product table needs to support efficient data retrieval to quickly obtain and display recommended products.
3. Data consistency: The design of the recommended product table needs to ensure the consistency and accuracy of the data to avoid data inconsistency during processing.

2. Recommended product table structure design
In MySQL, a relational database can be used to design and store the recommended product table. The following is a basic recommended product table structure design example:

CREATE TABLE recommendation (
id int(11) NOT NULL AUTO_INCREMENT,
user_id int(11) NOT NULL,
product_id int(11) NOT NULL,
score double NOT NULL,
timestamp timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (id),
KEY user_id (user_id),
KEY product_id (product_id),
KEY timestamp (timestamp)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

In the above design example, the recommended product table contains the following fields:

1. id: the primary key that uniquely identifies the recommended product.
2. user_id: The user's unique identifier, used to associate the user with recommended products.
3. product_id: The unique identifier of the recommended product, used to associate products and recommendations.
4. score: The score of recommended products, used to sort and filter recommended products.
5. timestamp: The timestamp of the recommended product, recording the time when the recommendation was generated.

3. Storage and operation of the recommended product table
In the mall application, the recommended product table needs to support the storage and operation of data. The following are some common examples of storage and operation operations:

1. Insert recommended products: You can use the INSERT INTO statement to insert new recommended product records into the recommended products table.

INSERT INTO recommendation(user_id,product_id,score)
VALUES (1, 1001, 0.8);

2. Query a user's recommended products: You can use the SELECT statement to query the user's recommended products based on the user ID.

SELECT * FROM recommendation WHERE user_id = 1;

3. Query recommended users of a product: You can use SELECT The statement queries the users who recommend the product based on the product ID.

SELECT * FROM recommendation WHERE product_id = 1001;

4. Recommended products sorted according to score: you can use the ORDER BY statement Recommended products are sorted according to their scores.

SELECT * FROM recommendation ORDER BY score DESC;

4. Extension of the recommended product table structure
The above recommendations The product table structure is a basic design example, and some fields and indexes can be expanded according to actual needs.

1. Product attributes: You can add related fields according to specific product attribute requirements, such as product category, product brand, product price, etc.
2. User attributes: You can add related fields according to changes in user attributes, such as user geographical location, user preference tags, user purchase history, etc.
3. Recommendation type: You can add related fields for different recommendation types, such as popular recommendations, personalized recommendations, collaborative filtering recommendations, etc.

By expanding the structure of the recommended product table, we can better meet different recommendation algorithms and business needs, and provide more accurate and personalized recommended products.

Summary:
This article introduces how to design the recommended product table structure of the mall in MySQL and provides specific code examples. A well-designed recommended product table structure can not only provide efficient data retrieval and operation, but also provide a better user experience and recommendation effect for the mall's recommendation system. By understanding the design principles and flexibly expanding the table structure, a more complex and powerful recommended product table can be designed according to actual needs.

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