Home PHP Framework Workerman How to use Workerman to implement a music recommendation system based on recommendation algorithms

How to use Workerman to implement a music recommendation system based on recommendation algorithms

Nov 07, 2023 am 11:58 AM
workerman Recommendation algorithm Music recommendations

How to use Workerman to implement a music recommendation system based on recommendation algorithms

How to use Workerman to implement a music recommendation system based on recommendation algorithms

Introduction:
With the development of the Internet, music recommendation systems play an important role in people’s daily lives. plays an increasingly important role. The recommendation system can recommend the most suitable music to users based on their interests and behavioral habits. This article will introduce how to use Workerman to implement a music recommendation system based on recommendation algorithms to help developers better understand and use Workerman.

1. Introduction to recommendation algorithm
The recommendation algorithm is the core of the music recommendation system. Common recommendation algorithms include content-based recommendation algorithms, collaborative filtering algorithms, and deep learning algorithms. In this article, we will explain the collaborative filtering algorithm as an example.

2. Use Workerman to build the backend of the recommendation system
Workerman is a high-performance PHP socket server framework, suitable for building real-time chat, games, push and other applications. We can use Workerman to build the backend of the music recommendation system and communicate with the frontend in real time.

  1. Install Workerman
    First, we need to execute the following command in the terminal to install Workerman:
composer require workerman/workerman
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  1. Create a simple socket server
    We can create a php file named recommended_server.php and write the following code in it:
<?php
require_once __DIR__.'/vendor/autoload.php';

use WorkermanWorker;

$worker = new Worker('websocket://0.0.0.0:8000');

$worker->count = 4;

$worker->onMessage = function($connection, $data) {
  // 接收到消息后的处理逻辑
  // 根据推荐算法生成音乐推荐结果
  // 将推荐结果发送给客户端
};

Worker::runAll();
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  1. Receive and process client requests
    In the above code, we define onMessage callback function, used to receive and process client requests. In actual development, we can parse the message sent by the client and call the corresponding recommendation algorithm function to generate music recommendation results.
  2. Start the server
    We can execute the following command in the terminal to start the Workerman server:
php recommend_server.php start
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3. Real-time communication on the front end
Real-time communication on the front end, We can use WebSocket technology. WebSocket is a protocol for full-duplex communication over a single TCP connection.

  1. Create a WebSocket connection
    We can use Javascript on the front end to create a WebSocket connection, as follows:
var socket = new WebSocket('ws://localhost:8000');

socket.onopen = function() {
  // 连接成功后的处理逻辑
  // 发送请求给后台
};

socket.onmessage = function(event) {
  // 接收到后台发送的推荐结果后的处理逻辑
  // 将推荐结果展示给用户
};

socket.onclose = function() {
  // 连接关闭后的处理逻辑
};

socket.onerror = function() {
  // 连接错误后的处理逻辑
};
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  1. Send a request to the background
    After the connection is successful, we can use the socket.send() method to send a request to the background, as shown below:
socket.send('request');
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  1. Receive the recommendation results sent by the background
    When the background generates recommendations The result will be sent to the front end via WebSocket. We can receive the recommended results in the onmessage event and display them to the user.

Conclusion:
This article introduces how to use Workerman to build a music recommendation system based on the recommendation algorithm. By combining recommendation algorithms and real-time communication technology, we can provide users with more accurate and personalized music recommendations. I hope this article can provide some help to developers when implementing similar recommendation systems.

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