How to conduct user behavior analysis and recommendation algorithm optimization of PHP flash sale system

How to conduct user behavior analysis and recommendation algorithm optimization of the PHP flash sale system

With the rapid development of the e-commerce industry, major e-commerce platforms are in order to attract users and promote For sales, limited time flash sale events are often held. For the PHP flash sale system, user behavior analysis and recommendation algorithm optimization are a key part. This article will introduce how to conduct user behavior analysis and recommendation algorithm optimization of the PHP flash sale system, and provide code examples.

1. User Behavior Analysis

User behavior analysis can help us understand users’ behavioral habits in flash sale activities, and then optimize the design and operating efficiency of the system. The following is a simple user behavior analysis code example:

// 记录用户秒杀行为
function recordUserAction($userId, $itemId) {
    // 连接数据库
    $conn = new mysqli("localhost", "username", "password", "database_name");
    if ($conn->connect_error) {
        die("数据库连接失败：" . $conn->connect_error);
    }
  
    // 记录用户行为
    $sql = "INSERT INTO user_action (user_id, item_id, action_time) VALUES ($userId, $itemId, NOW())";
    if ($conn->query($sql) === TRUE) {
        echo "用户行为记录成功";
    } else {
        echo "用户行为记录失败：" . $conn->error;
    }
  
    // 关闭数据库连接
    $conn->close();
}

// 根据用户ID获取用户行为记录
function getUserActions($userId) {
    // 连接数据库
    $conn = new mysqli("localhost", "username", "password", "database_name");
    if ($conn->connect_error) {
        die("数据库连接失败：" . $conn->connect_error);
    }
  
    // 查询用户行为记录
    $sql = "SELECT * FROM user_action WHERE user_id = $userId";
    $result = $conn->query($sql);
  
    if ($result->num_rows > 0) {
        // 输出每条行为记录
        while($row = $result->fetch_assoc()) {
            echo "行为ID：" . $row["action_id"]. " 用户ID：" . $row["user_id"]. " 商品ID：" . $row["item_id"]. "<br>";
        }
    } else {
        echo "没有找到用户的行为记录";
    }
  
    // 关闭数据库连接
    $conn->close();
}

2. Recommendation algorithm optimization

The optimization of the recommendation algorithm can help us provide users with more personalized and accurate recommendations As a result, user engagement and purchase rates increase. The following is a simple code example for recommendation algorithm optimization:

// 根据用户的行为记录进行推荐
function recommendItems($userId) {
    // 连接数据库
    $conn = new mysqli("localhost", "username", "password", "database_name");
    if ($conn->connect_error) {
        die("数据库连接失败：" . $conn->connect_error);
    }
  
    // 根据用户的行为记录进行推荐
    $sql = "SELECT item_id, COUNT(*) as count FROM user_action WHERE user_id = $userId GROUP BY item_id ORDER BY count DESC LIMIT 3";
    $result = $conn->query($sql);
  
    if ($result->num_rows > 0) {
        // 输出推荐的商品
        while($row = $result->fetch_assoc()) {
            echo "推荐商品ID：" . $row["item_id"]. " 点击次数：" . $row["count"]. "<br>";
        }
    } else {
         echo "没有找到推荐的商品";
    }
  
    // 关闭数据库连接
    $conn->close();
}

Through the above code example, we can see the importance of user behavior analysis and recommendation algorithm optimization for the PHP flash sale system. By analyzing user behavior, we can understand users' preferences and purchase intentions, and further optimize the design of the system. Through the optimization of recommendation algorithms, we can improve the accuracy of recommendation results and increase the possibility of user participation and purchase.

To sum up, user behavior analysis and recommendation algorithm optimization of the PHP flash sale system are key links to improve system operating efficiency and user purchase rate. By analyzing user behavior and optimizing recommendation algorithms, we can better understand user needs and behavioral habits, thereby providing a better shopping experience. I hope this article can be helpful to engineers who develop PHP flash sale systems.

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