Second-hand recycling website developed in PHP automatically recommends products that users may be interested in

The second-hand recycling website developed by PHP automatically recommends products that users may be interested in

With the continuous expansion and popularity of the second-hand recycling market, more and more people are beginning to pay attention to the utilization and value of second-hand goods. In order to help users better find second-hand products that meet their needs, it is particularly important to develop a function that can automatically recommend products that users may be interested in. This article will introduce how to use PHP to develop an automatic recommendation system for second-hand recycling websites, and attach corresponding code examples.

Data Collection

One of the keys to the automatic recommendation system is to accurately collect user behavior data and product information. Here, we can use user registration and login to obtain the user's basic information, such as the user's interests, hobbies, geographical location, etc. At the same time, we also need to obtain relevant information about the product, including product category, description, pictures, etc.

In PHP, we can use the MySQL database to store this data. The following is a sample code to create a user and product table:

// 创建用户表
CREATE TABLE users (
    id INT(11) AUTO_INCREMENT PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    password VARCHAR(255) NOT NULL,
    // 其他用户信息字段
);

// 创建商品表
CREATE TABLE products (
    id INT(11) AUTO_INCREMENT PRIMARY KEY,
    category VARCHAR(50) NOT NULL,
    description TEXT NOT NULL,
    // 其他商品信息字段
);

Data Analysis

After collecting relevant data about users and products, we need to analyze the data to find the relationship between users similarity and correlation between products. In this way, products that may be of interest to the user can be recommended more accurately.

A commonly used data analysis method is collaborative filtering. Collaborative filtering algorithms can find similar users by comparing behavioral data between users, such as purchase history and browsing history. Similarly, related products can also be found by comparing related data between products, such as the number of times purchased by users and ratings.

The following is a sample code that uses the collaborative filtering algorithm to calculate user similarity:

// 计算用户相似度
function getUserSimilarity($user1, $user2) {
    // 获取用户的共同浏览记录
    $commonViews = getCommonViews($user1, $user2);
    
    // 计算用户相似度
    $similarity = count($commonViews) / max(count(getViews($user1)), count(getViews($user2)));
    
    return $similarity;
}

// 获取用户的浏览记录
function getViews($user) {
    // 从数据库中获取用户的浏览记录
    $views = // 代码省略
    
    return $views;
}

// 获取用户的共同浏览记录
function getCommonViews($user1, $user2) {
    // 从数据库中获取用户的共同浏览记录
    $commonViews = // 代码省略
    
    return $commonViews;
}

Recommendation

Obtain the similarity between users and the correlation between products through data analysis After that, we can use recommendation algorithms to generate personalized recommendation results for users. Commonly used recommendation algorithms include content-based recommendations and collaborative filtering-based recommendations.

The following is a sample code that uses a recommendation algorithm based on collaborative filtering to generate recommended results for users:

// 为用户生成推荐结果
function generateRecommendations($user) {
    // 获取与用户相似度最高的用户
    $similarUser = getSimilarUser($user);
    
    // 获取与用户相似度最高的用户的浏览记录
    $similarUserViews = getViews($similarUser);
    
    // 获取用户未浏览过的商品
    $unseenProducts = getUnseenProducts($user, $similarUserViews);
    
    // 获取用户可能感兴趣的商品
    $recommendations = getTopNRecommendations($user, $unseenProducts);
    
    return $recommendations;
}

// 获取与用户相似度最高的用户
function getSimilarUser($user) {
    // 从数据库中获取与用户相似度最高的用户
    $similarUser = // 代码省略
    
    return $similarUser;
}

// 获取用户未浏览过的商品
function getUnseenProducts($user, $similarUserViews) {
    // 从数据库中获取用户未浏览过的商品
    $unseenProducts = // 代码省略
    
    return $unseenProducts;
}

// 获取用户可能感兴趣的商品
function getTopNRecommendations($user, $unseenProducts) {
    // 从数据库中获取用户可能感兴趣的商品
    $recommendations = // 代码省略
    
    return $recommendations;
}

Summary

Through the above code examples, we can see how to use PHP develops an automatic recommendation system for second-hand recycling websites. From data collection to collaborative filtering analysis to recommendation result generation, we gradually implemented a basic automatic recommendation function. Of course, this is just a simple example, and actual development requires appropriate optimization and expansion based on specific needs.

I hope this article can provide some reference and help to PHP developers in the development of automatic recommendation systems for second-hand recycling websites. We believe that through continuous optimization and improvement, we can provide users with more accurate and personalized second-hand product recommendation services.

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