How to use PHP to build a personalized recommendation system and user portrait

How to use PHP to build a personalized recommendation system and user portraits

Introduction:
In the Internet era, personalized recommendation systems and user portraits have become important means for major enterprises to improve user experience and precision marketing. . The combination of the two can provide users with personalized recommended content and bring better business results to enterprises. This article will introduce how to use PHP to build a personalized recommendation system and user portraits to help developers better understand and apply these two key technologies.

1. Personalized recommendation system
The core idea of ​​the personalized recommendation system is to provide recommended content related to the user's personal preferences based on the user's historical behavior and interests. The following takes a personalized recommendation system based on collaborative filtering algorithm as an example to introduce how to build it using PHP.

1. Data collection and preprocessing
   First of all, it is necessary to collect the user's historical behavior data, such as user clicks, purchases, collections, etc. These data can be collected through websites or APPs on the Internet. The collected data can be stored in a database or file for subsequent use.

Code example 1:

// 假设收集到的数据存储在数据库中，可以使用PDO进行操作
$db = new PDO('mysql:host=localhost;dbname=test', 'username', 'password');
$stmt = $db->prepare("INSERT INTO user_behavior (user_id, item_id, action) VALUES (:user_id, :item_id, :action)");
$stmt->bindParam(':user_id', $user_id);
$stmt->bindParam(':item_id', $item_id);
$stmt->bindParam(':action', $action);

// 获取用户行为数据
$user_id = 1;
$item_id = 1001;
$action = 'click';
$stmt->execute();

2. Similarity calculation
   The personalized recommendation system based on collaborative filtering algorithm needs to calculate the similarity between users based on user behavior data . Commonly used calculation methods include Euclidean distance, cosine similarity, etc.

Code example 2:

// 计算用户之间的相似度，可以使用余弦相似度
function cosine_similarity($vector1, $vector2) {
    $sum = 0;
    $dot_product = 0;
    $length1 = 0;
    $length2 = 0;

    foreach ($vector1 as $value) {
        $length1 += pow($value, 2);
    }

    foreach ($vector2 as $value) {
        $length2 += pow($value, 2);
    }

    foreach ($vector1 as $key => $value) {
        if (isset($vector2[$key])) {
            $dot_product += $value * $vector2[$key];
        }
    }

    $length1 = sqrt($length1);
    $length2 = sqrt($length2);

    if ($length1 * $length2 != 0) {
        return $dot_product / ($length1 * $length2);
    } else {
        return 0;
    }
}

3. Recommended content generation
   Based on the calculated similarity, personalized recommended content can be generated for users. The recommendation score can be calculated based on the items in the user's historical behavior and the behavior of similar users, and sorted according to the score. Items with high recommendation scores generate a recommendation list for the user.

Code example 3:

// 为用户生成推荐内容
function generate_recommendation($user_id) {
    $recommendations = array();

    // 获取用户的历史行为数据
    $user_behavior = get_user_behavior($user_id);

    // 获取与用户相似的用户
    $similar_users = get_similar_users($user_id);

    // 遍历与用户相似的用户的历史行为
    foreach ($similar_users as $sim_user) {
        $sim_user_behavior = get_user_behavior($sim_user);

        // 计算推荐得分
        foreach ($sim_user_behavior as $item_id => $action) {
            if (!isset($user_behavior[$item_id])) {
                if (!isset($recommendations[$item_id])) {
                    $recommendations[$item_id] = 0;
                }
                $recommendations[$item_id] += $action * cosine_similarity($user_behavior, $sim_user_behavior);
            }
        }
    }

    // 按照推荐得分进行排序
    arsort($recommendations);

    return $recommendations;
}

2. User portrait
User portrait is to build the user’s characteristic model based on the user’s personal information and behavioral data for better Understand and analyze user needs and preferences. The following takes user portraits based on user behavior data as an example to introduce how to build them using PHP.

1. User feature extraction
   According to the user’s behavioral data, the user’s features can be extracted. User characteristics can include age, gender, interest tags, etc. The extracted features can be stored in the database for subsequent use.

Code example 4:

// 抽取用户特征
function extract_user_features($user_id) {
    $user_features = array();

    $user_behavior = get_user_behavior($user_id);

    // 根据用户行为数据抽取特征
    foreach ($user_behavior as $item_id => $action) {
        // 假设item_id对应的物品是有标签的
        $item_tags = get_item_tags($item_id);

        // 将标签加入用户特征中
        foreach ($item_tags as $tag) {
            if (!isset($user_features[$tag])) {
                $user_features[$tag] = 0;
            }
            $user_features[$tag] += $action;
        }
    }

    return $user_features;
}

2. User portrait generation
   User portraits can be generated for users based on the extracted user characteristics. User portraits can include the user's age, gender, interest tags, etc.

Code Example 5:

// 生成用户画像
function generate_user_profile($user_id) {
    $user_profile = array(
        'age' => get_user_age($user_id),
        'gender' => get_user_gender($user_id),
        'interests' => array(),
    );

    $user_features = extract_user_features($user_id);

    // 根据用户特征生成用户画像
    $user_profile['interests'] = array_keys($user_features, max($user_features));

    return $user_profile;
}

Conclusion:
Through the introduction of this article, we have learned how to use PHP to build a personalized recommendation system and user portraits. The personalized recommendation system can provide personalized recommendation content based on the user's historical behavior; the user portrait can generate the user's characteristic model based on the user's personal information and behavioral data. The combination of the two can help companies better understand user needs, improve user experience and precision marketing effects. In practical applications, machine learning and other technologies can also be combined to further optimize and improve the effects of personalized recommendation systems and user portraits.

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