How to use Workerman to implement a real-time recommendation system based on user behavior

Title: Using Workerman to implement a real-time recommendation system based on user behavior

Introduction:
With the rapid development of the Internet, the amount of data generated by users continues to increase. How to use this data to provide users with personalized recommendation services has become an important issue. The real-time recommendation system makes recommendations based on the user's current behavioral data and provides users with real-time personalized recommendations. This article will introduce how to use the PHP framework Workerman to implement a real-time recommendation system, including system architecture, database design, recommendation algorithm and code examples.

Part One: System Architecture Design
1.1 User Behavior Collection Module:
User behavior information, such as browsing products, purchasing products, etc., is collected through JavaScript or other methods, and requests are sent to the backend.

1.2 Data storage module:
User behavior data is stored in the database, including user information, product information, and interaction information between users and products.

1.3 Real-time recommendation module:
Through real-time analysis and calculation of user behavior data, real-time recommendation results for users are generated, and the results are returned to the front-end for display.

Part 2: Database Design
2.1 User information table:
Contains the basic information of the user, such as user ID, name, gender, etc.

2.2 Product information table:
Contains basic information about the product, such as product ID, name, price, etc.

2.3 User behavior table:
Records the interaction information between users and products, including user ID, product ID, behavior type (browsing, purchase, etc.), behavior time, etc.

Part 3: Recommendation Algorithm
3.1 Recommendation algorithm based on collaborative filtering:
It can calculate the similarity between users and recommend products that other users like with similar behaviors to the user.

3.2 Recommendation algorithm based on content filtering:
By analyzing the characteristics of products that users like, recommend other products that are similar to these characteristics.

3.3 Hybrid recommendation algorithm:
Use multiple recommendation algorithms comprehensively and take advantage of the advantages of different algorithms to improve recommendation accuracy.

Part 4: Code Example
The following is a code example using Workerman to implement a real-time recommendation system:

require_once DIR . ' /vendor/autoload.php';

use WorkermanWorker;

// Create a Worker to listen to port 9000
$worker = new Worker('websocket://0.0.0.0:9000 ');

//Set the number of processes to 4
$worker->count = 4;

//Real-time recommendation processing logic
$worker->onMessage = function($connection, $data) {

// 从推荐模块获取实时推荐结果
$result = getRealTimeRecommend($data);

// 将推荐结果返回给前端
$connection->send(json_encode($result));

};

// Start Worker
Worker::runAll();

// Get real-time recommendation results The function
function getRealTimeRecommend($data) {

// 解析前端发送的数据
$user = json_decode($data, true);

// 根据用户行为数据进行实时推荐计算

// 返回推荐结果
return $recommendResult;

}
?>

Conclusion:
This article introduces how to use the Workerman framework to achieve real-time based on user behavior Recommendation system, including system architecture, database design, recommendation algorithm and code examples. Through this real-time recommendation system, users can be provided with personalized, real-time recommendation services, improving user experience and product sales. At the same time, readers can further improve and customize their own real-time recommendation system based on these sample codes.

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