PHP と機械学習: インテリジェントなレコメンデーション システムを実装する方法

<?php
// 连接数据库
$servername = "localhost";
$username = "username";
$password = "password";
$dbname = "database";
$conn = new mysqli($servername, $username, $password, $dbname);
if ($conn->connect_error) {
    die("连接数据库失败: " . $conn->connect_error);
}

// 用户历史数据
$user_id = 1; // 用户ID
$item_id = 1; // 商品ID

// 将用户历史数据插入数据库
$sql = "INSERT INTO user_history (user_id, item_id) VALUES ('$user_id', '$item_id')";
if ($conn->query($sql) === TRUE) {
    echo "用户历史数据插入成功";
} else {
    echo "Error: " . $sql . "<br>" . $conn->error;
}

// 关闭数据库连接
$conn->close();
?>

<?php
require 'vendor/autoload.php';

use PhpmlCollaborativeFilteringNeighborhood;
use PhpmlCollaborativeFilteringRatingMatrix;
use PhpmlMathMatrix;
use PhpmlMathStatisticMean;

// 用户评分矩阵
$ratings = new RatingMatrix([
    [3, 4, 0, 3, 2],
    [4, 3, 1, 5, 5],
    [1, 2, 4, 0, 3],
    [4, 4, 0, 4, 2],
]);

// 计算用户之间的相似度
$similarityMatrix = new Matrix($ratings->userSimilarities());

// 找到最相似的用户
$bestMatches = Neighborhood::findBestMatches($similarityMatrix->toArray(), 0);

// 根据最相似的用户生成推荐
$user = 0; // 用户ID
$recommendations = Neighborhood::userBased($user, $ratings->toArray(), $bestMatches, 3);

// 输出推荐结果
echo "用户 " . $user . "的推荐结果：";
foreach ($recommendations as $item => $rating) {
    echo "商品 " . $item . "，评分：" . $rating . "<br>";
}
?>

<?php
require 'vendor/autoload.php';

use PhpmlCrossValidationCrossValidation;
use PhpmlDatasetDemoWineDataset;
use PhpmlMetricAccuracy;
use PhpmlClassificationSVC;

// 加载示例数据集
$dataset = new WineDataset();

// 划分数据集为训练集和测试集
$cv = new CrossValidation($dataset, $classifier = new SVC(), 5);

// 计算模型的准确性
$accuracy = Accuracy::score($cv->getTestLabels(), $cv->getPredictedLabels());

// 输出准确性结果
echo "模型的准确性：" . $accuracy;
?>