Analysis of shopping mall user browsing footprint records developed using PHP

Analysis of browsing footprint records of mall users developed using PHP

With the rapid development of e-commerce, mall websites have become one of the main channels for people to shop. For malls, it is very important to understand and analyze users' browsing footprints. It can help malls understand users' shopping interests and behavioral habits, so as to make more accurate recommendations and personalized services based on user needs. This article will introduce how to use the mall user browsing footprint record analysis system developed in PHP.

First, we need to create a database to store the user's browsing footprint records. Suppose we have two tables: users and browsing_history. users The table is used to store basic information of users, including user ID, user name, etc. browsing_history The table is used to store the user's browsing history, including record ID, user ID, product ID, browsing time, etc.

The following is a sample code to create the users table:

CREATE TABLE users (
    id INT AUTO_INCREMENT PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    email VARCHAR(50) NOT NULL,
    password VARCHAR(255) NOT NULL
);

The following is a sample code to create the browsing_history table:

CREATE TABLE browsing_history (
    id INT AUTO_INCREMENT PRIMARY KEY,
    user_id INT NOT NULL,
    product_id INT NOT NULL,
    viewed_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (user_id) REFERENCES users(id),
    FOREIGN KEY (product_id) REFERENCES products(id)
);

Next, we need to write PHP code to implement the user's browsing footprint recording function. First, we need to write a function to record the user's browsing operations. The following is a sample code:

function recordBrowsingHistory($userId, $productId) {
    // 连接数据库
    $conn = mysqli_connect("localhost", "username", "password", "database");

    // 检查数据库连接是否成功
    if (!$conn) {
        die("数据库连接失败: " . mysqli_connect_error());
    }

    // 插入浏览记录到数据库
    $sql = "INSERT INTO browsing_history (user_id, product_id) VALUES ('$userId', '$productId')";
    if (mysqli_query($conn, $sql)) {
        echo "浏览记录插入成功";
    } else {
        echo "插入错误: " . mysqli_error($conn);
    }

    // 关闭数据库连接
    mysqli_close($conn);
}

Then, we can call this function on the product details page of the mall website to record the user's browsing footprint. For example:

// 获取当前登录用户的ID
$userId = $_SESSION['user_id'];

// 获取当前浏览商品的ID
$productId = $_GET['product_id'];

// 调用记录浏览足迹的函数
recordBrowsingHistory($userId, $productId);

Finally, we can write PHP code to analyze the user's browsing footprint records to obtain the user's shopping interests and behavioral habits. The following is a sample code:

function analyzeBrowsingHistory($userId) {
    // 连接数据库
    $conn = mysqli_connect("localhost", "username", "password", "database");

    // 检查数据库连接是否成功
    if (!$conn) {
        die("数据库连接失败: " . mysqli_connect_error());
    }

    // 查询用户的浏览足迹记录
    $sql = "SELECT product_id FROM browsing_history WHERE user_id = '$userId'";
    $result = mysqli_query($conn, $sql);

    // 统计每个商品的浏览次数
    $productViews = array();
    while ($row = mysqli_fetch_assoc($result)) {
        $productId = $row['product_id'];
        if (!isset($productViews[$productId])) {
            $productViews[$productId] = 1;
        } else {
            $productViews[$productId]++;
        }
    }

    // 根据浏览次数排序商品
    arsort($productViews);

    // 输出购物兴趣排行榜
    echo "购物兴趣排行榜：<br>";
    foreach ($productViews as $productId => $views) {
        echo "商品ID：$productId，浏览次数：$views<br>";
    }

    // 关闭数据库连接
    mysqli_close($conn);
}

Call the analyzeBrowsingHistory function to analyze the user's browsing footprint records and output the shopping interest rankings.

The above are relevant code examples of the shopping mall user browsing footprint record analysis system developed using PHP. By recording and analyzing users' browsing footprints, malls can better understand users' shopping interests and behavioral habits, provide more personalized recommendations and services, and thereby improve user experience and sales results.

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