How to use PHP to implement click-through rate estimation and advertising recommendation models

How to use PHP to implement click-through rate estimation and advertising recommendation models

Click-through rate estimation and advertising recommendation models are very important technologies in the field of Internet advertising. Click-through rate estimation can help advertisers better estimate the number of clicks on advertisements and thus allocate advertising resources appropriately. The advertising recommendation model can recommend appropriate ads based on the user's interests and behavior, improving the conversion rate of advertising. This article will introduce how to use PHP language to implement click-through rate estimation and advertising recommendation models, and attach code examples to help readers better understand and apply.

1. Click-through rate prediction model

The click-through rate prediction model predicts the user's click-through rate on the advertisement based on the user's historical behavior and the characteristics of the advertisement. Commonly used click-through rate prediction models include linear regression models, logistic regression models, and gradient boosting decision tree models.

The following is an example of PHP code that uses a logistic regression model to achieve click-through rate estimation:

<?php
// 训练数据
$trainingData = [
    [2, 0, 1, 1],
    [3, 1, 1, 0],
    [1, 1, 0, 1],
    [4, 0, 1, 0],
];

// 训练目标
$targets = [1, 0, 1, 0];

// 载入逻辑回归模型库
require_once('LogisticRegression.php');

// 初始化逻辑回归模型
$model = new LogisticRegression();

// 使用训练数据训练模型
$model->train($trainingData, $targets);

// 预测新数据
$newData = [2, 0, 0, 1];
$prediction = $model->predict($newData);

// 输出预测结果
echo "点击率预估：" . $prediction;
?>

In the above code, we use a training data set and the corresponding target value to train the logic regression model. Then, we can use the trained model to predict the new data and get the estimated click-through rate.

2. Advertising recommendation model

The advertising recommendation model recommends appropriate ads to users based on their interests and behavioral characteristics. Commonly used advertising recommendation models include collaborative filtering models, content recommendation models, and deep learning models.

The following is an example of PHP code that uses the collaborative filtering model to implement advertising recommendation:

<?php
// 用户-广告兴趣矩阵
$interestMatrix = [
    [1, 0, 1, 0],
    [0, 1, 0, 1],
    [1, 1, 0, 0],
];

// 广告-特征矩阵
$featureMatrix = [
    [1, 0, 1, 0],
    [0, 1, 0, 1],
    [1, 0, 0, 1],
    [0, 1, 1, 0],
];

// 计算用户和广告之间的相似度
function similarity($user, $ad) {
    $numerator = 0;
    $denominator = 0;
    for ($i = 0; $i < count($user); $i++) {
        $numerator += $user[$i] * $ad[$i];
        $denominator += pow($user[$i], 2) * pow($ad[$i], 2);
    }
    return $numerator / sqrt($denominator);
}

// 为用户推荐广告
function recommend($interestMatrix, $featureMatrix, $user) {
    $recommendations = [];
    for ($i = 0; $i < count($featureMatrix); $i++) {
        $similarity = similarity($interestMatrix[$user], $featureMatrix[$i]);
        array_push($recommendations, $similarity);
    }
    return $recommendations;
}

// 设置用户
$user = 0;

// 获取广告推荐列表
$recommendations = recommend($interestMatrix, $featureMatrix, $user);

// 输出推荐结果
echo "广告推荐列表：" . implode(", ", $recommendations);
?>

In the above code, we first define the user-advertising interest matrix and advertising-feature matrix, and then pass Calculate the similarity between users and ads to recommend ads to users. Finally, we can get the recommendation list and output the results.

Summary:

This article introduces how to use PHP to implement click-through rate estimation and advertising recommendation models, and attaches corresponding code examples. These models can help advertisers better estimate the number of clicks on ads and recommend appropriate ads, thereby improving advertising effectiveness and conversion rates. Readers can further improve the effectiveness of Internet advertising by learning and applying these models. Hope this article can be helpful to readers!

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