Build a PHP mall: implement product recommendation and personalized recommendation functions

Building a PHP mall: realizing product recommendation and personalized recommendation functions

In today's e-commerce field, users' demand for personalized recommendations is getting higher and higher. Through personalized recommendations, merchants can recommend products that best suit users’ interests and needs based on their shopping behavior and preferences, thereby improving user experience and sales conversion rate. This article will introduce how to use PHP to build a shopping mall website with product recommendation and personalized recommendation functions.

1. Database design

First, we need to design a database model to store product and user information.

We can create two tables, one table is used to store product information, including fields: product ID, product name, product description, product price, etc.

CREATE TABLE products (
    id INT PRIMARY KEY,
    name VARCHAR(255),
    description TEXT,
    price DECIMAL(8, 2),
);

Another table is used to store user information, including fields: user ID, user name, password, etc.

CREATE TABLE users (
    id INT PRIMARY KEY,
    username VARCHAR(255),
    password VARCHAR(255),
);

2. Product recommendation function

In order to implement the product recommendation function, we can recommend related products based on the user's historical purchase records or browsing behavior.

Assuming that after the user logs in, we can obtain the user's purchase record:

$user_id = $_SESSION['user_id'];

// 获取用户购买记录
$sql = "SELECT product_id FROM purchases WHERE user_id = $user_id";
$result = $conn->query($sql);

$purchase_history = [];
if ($result->num_rows > 0) {
    while ($row = $result->fetch_assoc()) {
        $purchase_history[] = $row['product_id'];
    }
}

Then, we can query the products related to the user's purchase history from the product table based on the user's purchase record ：

// 获取相关商品
$sql = "SELECT * FROM products WHERE id IN (" . implode(",", $purchase_history) . ") LIMIT 5";
$result = $conn->query($sql);

if ($result->num_rows > 0) {
    // 显示相关商品
    while ($row = $result->fetch_assoc()) {
        echo $row['name'] . "<br>";
    }
} else {
    echo "暂无相关商品";
}

3. Personalized recommendation function

The personalized recommendation function needs to make recommendations based on the user’s interests and preferences. We can analyze the user's interests based on the user's behavioral data, such as browsing history, search history, etc., and then recommend relevant products to them.

Assume that when a user browses products, we can record his browsing history:

$product_id = $_GET['product_id'];
$user_id = $_SESSION['user_id'];

// 记录用户浏览记录
$sql = "INSERT INTO views (user_id, product_id) VALUES ($user_id, $product_id)";
$conn->query($sql);

Then, we can query the products related to his interests from the product table based on the user's browsing history:

// 获取用户的浏览记录
$sql = "SELECT product_id FROM views WHERE user_id = $user_id";
$result = $conn->query($sql);

$view_history = [];
if ($result->num_rows > 0) {
    while ($row = $result->fetch_assoc()) {
        $view_history[] = $row['product_id'];
    }
}

// 获取个性化推荐商品
$sql = "SELECT * FROM products WHERE id IN (" . implode(",", $view_history) . ") LIMIT 5";
$result = $conn->query($sql);

if ($result->num_rows > 0) {
    // 显示个性化推荐商品
    while ($row = $result->fetch_assoc()) {
        echo $row['name'] . "<br>";
    }
} else {
    echo "暂无个性化推荐商品";
}

Through the above code examples, we can implement product recommendation and personalized recommendation functions. The mall website can recommend relevant products to users based on their purchasing and browsing behaviors, improving user experience and sales conversion rate. Of course, the above is just a simple example. The actual personalized recommendation algorithm will be more complex and needs to be designed according to specific needs and business.

The above is the detailed content of Build a PHP mall: implement product recommendation and personalized recommendation functions. For more information, please follow other related articles on the PHP Chinese website!