Workerman을 사용하여 협업 필터링 기반의 영화 추천 시스템을 구현하는 방법

UserID | MovieID | Rating | Timestamp
---------------------------------------
   196  |    242  |      3 | 881250949
   186  |    302  |      3 | 891717742
   196  |    377  |      1 | 878887116
...

class UserModel
{
    public static function getRatings($userId)
    {
        $ratings = array();
        $file = fopen('u.data', 'r');
        while (($line = fgets($file)) !== false) {
            $data = explode("    ", trim($line));
            if ($userId == $data[0]) {
                $ratings[$data[1]] = $data[2]; // 记录该用户对该电影的评分
            }
        }
        fclose($file);
        return $ratings;
    }
}

class CFModel
{
    public static function predictRating($userId, $movieId)
    {
        $simUsers = array(); // 相似用户ID列表
        $simValues = array(); // 相似值列表
        $ratings1 = UserModel::getRatings($userId);
        if (empty($ratings1)) {
            return 0;
        }
        $file = fopen('u.data', 'r');
        while (($line = fgets($file)) !== false) {
            $data = explode("    ", trim($line));
            if ($userId != $data[0] && $movieId == $data[1]) { // 如果不是当前用户且电影相同
                $ratings2 = UserModel::getRatings($data[0]);
                if (!empty($ratings2)) { // 如果相似用户有评分记录
                    $sim = self::similarity($ratings1, $ratings2); // 计算相似度
                    if ($sim > 0) { // 如果相似度大于0
                        $simUsers[] = $data[0];
                        $simValues[] = $sim;
                    }
                }
            }
        }
        fclose($file);
        if (empty($simUsers)) {
            return 0;
        }
        arsort($simValues); // 按相似度从高到低排序
        $simUsers = array_slice($simUsers, 0, 10); // 取相似度最高的10个用户
        $simValues = array_slice($simValues, 0, 10); // 取相似度最高的10个用户的相似度值
        $sum = 0;
        $weight = 0;
        foreach ($simUsers as $k => $simUser) {
            $rating = UserModel::getRatings($simUser)[$movieId]; // 获取相似用户对该电影的评分
            $sum += $simValues[$k] * $rating; // 计算评分总和
            $weight += $simValues[$k]; // 计算权重总和
        }
        return round($sum / $weight); // 计算平均评分
    }

    public static function similarity($ratings1, $ratings2)
    {
        $commonKeys = array_keys(array_intersect_key($ratings1, $ratings2));
        if (empty($commonKeys)) {
            return 0;
        }
        $diff1 = $diff2 = 0;
        foreach ($commonKeys as $key) {
            $diff1 += ($ratings1[$key] - $ratings2[$key]) ** 2;
            $diff2 += ($ratings1[$key] - $ratings2[$key]) ** 2;
        }
        return $diff1 / sqrt($diff2);
    }
}

use WorkermanProtocolsHttpRequest;
use WorkermanProtocolsHttpResponse;
use WorkermanWorker;

require_once __DIR__ . '/vendor/autoload.php';

$http_worker = new Worker("http://0.0.0.0:8888");

$http_worker->onMessage = function(Request $request, Response $response) {
    $userId = $request->get('userId');
    $movieId = $request->get('movieId');
    $rating = CFModel::predictRating($userId, $movieId);
    $response->header('Content-Type', 'application/json');
    $response->end(json_encode(array('rating' => $rating)));
};

Worker::runAll();

http://localhost:8888?userId=1&movieId=1