How to use PHP to implement the intelligent recommendation function of CMS system

How to use PHP to implement the intelligent recommendation function of CMS system

With the rapid development of the Internet and the explosive growth of information, users are faced with a large number of information choices when browsing the web. In order to improve user experience and website stickiness, the intelligent recommendation function in content management systems (CMS) has become increasingly important. This article will introduce how to implement a simple but efficient CMS system intelligent recommendation function through PHP.

1. Data model design
   First, we need to design a data model to store articles and user behavior data. A simple data model can include the following tables:

• Article table (articles): stores relevant information about articles, such as article ID, title, content, etc.
• User table (users): stores basic information of users, such as user ID, user name, password, etc.
• User behavior table (user_actions): records user behavior, such as user ID, article ID, behavior type (browse, like, favorite, etc.), behavior time, etc.

2. Data collection and processing
   In order to implement the intelligent recommendation function, we need to collect user behavior data and store it in the user behavior table. This can be achieved by adding the corresponding JavaScript code on the article page. When the user browses the article, the JavaScript code will send a request to the background and store the user's behavior data in the database. With enough user behavior data, we can proceed to the next step of the recommendation algorithm.
3. Recommendation algorithm implementation
   In the intelligent recommendation function, the most commonly used algorithm is the collaborative filtering algorithm. The collaborative filtering algorithm analyzes user behavior data to find users with similar interests and hobbies, and recommends articles that these users like to the current user.

The following is a simple PHP code example for recommending articles based on user behavioral data:

// 获取当前用户的ID
$user_id = $_SESSION['user_id'];

// 查询用户曾经浏览过的文章
$query = "SELECT DISTINCT article_id FROM user_actions WHERE user_id = '$user_id' AND action_type = 'view'";
$result = mysqli_query($conn, $query);

// 构建已浏览文章的数组
$viewed_articles = array();
while ($row = mysqli_fetch_assoc($result)) {
    $viewed_articles[] = $row['article_id'];
}

// 查询与已浏览文章相似的其他用户浏览过的文章
$query = "SELECT DISTINCT article_id FROM user_actions WHERE user_id != '$user_id' AND action_type = 'view' AND article_id IN (SELECT article_id FROM user_actions WHERE user_id = '$user_id' AND action_type = 'view')";
$result = mysqli_query($conn, $query);

// 构建相似文章的数组
$similar_articles = array();
while ($row = mysqli_fetch_assoc($result)) {
    $similar_articles[] = $row['article_id'];
}

// 查询推荐的文章
$query = "SELECT * FROM articles WHERE article_id IN (SELECT DISTINCT article_id FROM user_actions WHERE user_id != '$user_id' AND action_type = 'view' AND article_id NOT IN (" . implode(',', $viewed_articles) .") AND article_id IN (" . implode(',', $similar_articles) . "))";
$result = mysqli_query($conn, $query);

// 输出推荐的文章
while ($row = mysqli_fetch_assoc($result)) {
    echo $row['title'];
    echo $row['content'];
}

4. Page display
   Finally, we need to display the content in the CMS Recommended articles are displayed on the system's page. Based on the above code example, we can add a recommendation module to the sidebar or bottom of the article page to display related articles recommended based on the user's behavioral data.

Summary:
This article introduces how to implement a simple but efficient CMS system intelligent recommendation function through PHP. By collecting user behavior data, designing appropriate data models and using collaborative filtering algorithms, we can provide users with personalized recommendation services and improve user experience and website stickiness. Of course, this is just a simple example. An actual intelligent recommendation system may need to consider more factors, such as the popularity of articles, user interest tags, etc. I hope this article will help you understand the implementation of the intelligent recommendation function.

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