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

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

Recommendation systems play a vital role in modern e-commerce platforms and social media applications, which can improve user experience, increase user stickiness and promote sales. In the recommendation system, a key part is to recommend related books based on the user's interests and behavior data.

Before designing a high-performance MySQL table structure, we need to determine the data type and structure to be stored in the table. In this case, we need to consider the basic information of the book (such as book title, author, publisher, etc.), as well as user behavior data (such as user clicks, browsing and purchase records).

1. Create books table (books)

First, we need to create a books table (books) to store all book information. The table should contain the following fields:

• id: The unique identifier of the book, using an auto-incrementing primary key.
• title: The title of the book.
• author: The author of the book.
• publisher: The publisher of the book.
• ...

Sample code:

CREATE TABLE books (
  id INT AUTO_INCREMENT PRIMARY KEY,
  title VARCHAR(255) NOT NULL,
  author VARCHAR(255),
  publisher VARCHAR(255),
  ...
);

2. Create user action table (user_actions)

Next, We need to create a user action table (user_actions) to store user behavior data for books. The table should contain the following fields:

• id: A unique identifier for the action, using an auto-incrementing primary key.
• user_id: The unique identifier of the user, which is associated with the user table through a foreign key.
• book_id: The unique identifier of the book, which is associated with the book table through a foreign key.
• action: The user’s behavior type, such as click, browse or purchase.
• timestamp: The timestamp when the behavior occurred.

Sample code:

CREATE TABLE user_actions (
  id INT AUTO_INCREMENT PRIMARY KEY,
  user_id INT,
  book_id INT,
  action VARCHAR(10),
  timestamp DATETIME,
  FOREIGN KEY (user_id) REFERENCES users(id),
  FOREIGN KEY (book_id) REFERENCES books(id)
);

3. Create recommendation result table (recommendations)

Finally, we create a recommendation result table (recommendations) to Stores recommended books based on user behavior. The table should contain the following fields:

• id: The unique identifier of the recommended result, using an auto-incrementing primary key.
• user_id: The unique identifier of the user, which is associated with the user table through a foreign key.
• book_id: The unique identifier of the book, which is associated with the book table through a foreign key.
• score: Recommended score, used to sort recommendation results.

Sample code:

CREATE TABLE recommendations (
  id INT AUTO_INCREMENT PRIMARY KEY,
  user_id INT,
  book_id INT,
  score FLOAT,
  FOREIGN KEY (user_id) REFERENCES users(id),
  FOREIGN KEY (book_id) REFERENCES books(id)
);

Through the above table structure design, we can use MySQL to implement the high-performance recommended book function. When users perform related behaviors (such as clicking, browsing, and purchasing), we can collect and record these behavioral data through the user behavior table (user_actions), generate recommendation results based on these data, and store the results in the recommendation results table ( recommendations) for subsequent reading and display.

In practical applications, we can further improve query and recommendation performance through reasonable index design. For example, a joint index is created based on user ID, behavior type and timestamp to speed up querying of behavioral data and generation of recommendation results.

In short, through reasonable design of MySQL table structure and index, combined with appropriate data processing algorithms, we can achieve high-performance book recommendation function. In fact, this is just a basic example of a recommendation system, and the specific implementation needs to be further optimized and adjusted according to specific business needs and scale.

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