How to use Workerman to implement a movie recommendation system based on collaborative filtering

With the continuous development of Internet technology, more and more websites and applications are beginning to focus on user experience and personalized recommendations. The recommendation system is an extremely important part of it. It can recommend content that best suits the user's interests based on the user's historical behavior and preferences. This article will introduce how to use the Workerman framework to implement a movie recommendation system based on collaborative filtering.

1. Collaborative filtering algorithm

Collaborative filtering is one of the most commonly used algorithms in recommendation systems. It predicts the user's rating of unknown items or whether they will like this based on the user's historical behavior and preferences. thing. The basic idea of ​​the collaborative filtering algorithm is to discover the similarities between users and the similarities between items. Among them, the similarity between users can be realized by calculating the similarity of users' historical ratings, and the similarity between items can be realized by calculating the ratings of different users on different items.

2. Introduction to Workerman framework

Workerman is a high-performance network communication framework developed purely in PHP. It adopts an asynchronous non-blocking IO model and has the characteristics of high concurrency, high performance, and low energy consumption. , can handle a large number of high-concurrency long connections, and can be used to implement distributed, instant messaging, online games, Internet of Things and other scenarios.

3. Use Workerman to implement a movie recommendation system based on collaborative filtering

1. Data preparation

First, we need to prepare the movie rating data set, data The set contains the user ID, movie ID, and the user's rating for the movie. The data set can be downloaded from the MovieLens website, for example, download the ml-100k.zip package. After decompression, you can get the u.data file, which contains 100,000 rating records. The format of the data set is as follows:

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

2. Establish a user rating model

According to the movie rating data set, we can establish a user rating model, which can query the user based on the user ID Ratings for all movies. The following is a simple example of a user rating model:

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;
    }
}

3. Establishing a collaborative filtering model

According to the established user rating model, we can establish a collaborative filtering model, which can Predict the user's rating for an unknown movie based on the user's historical ratings. The following is a simple collaborative filtering model example:

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);
    }
}

4. Establishing a recommendation system service

Based on the above collaborative filtering model, we can build a recommendation system service that can Receives the user ID and movie ID as parameters and returns the user's predicted rating for the movie. The following is a simple recommendation system service example:

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();

5. Testing the recommendation system service

Finally, we can test the recommendation system service by sending an HTTP request, for example:

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

This request will return a JSON-formatted response containing the user's predicted rating for the movie.

IV. Summary

This article introduces how to use the Workerman framework to implement a movie recommendation system based on collaborative filtering. This system can predict the user's rating of unknown movies based on the user's historical behavior and preferences. The code example is just a simple implementation. In actual applications, many factors need to be considered, such as data size, algorithm optimization, model training, etc. I hope this article can help readers understand and implement recommendation systems.

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