How to implement recommendation algorithm with PHP

How to use PHP to implement recommendation algorithms

Introduction:
Recommendation algorithms play an important role in today's Internet applications. It can provide users with personalized recommendations based on their behavior and preferences. content. PHP, as a widely used scripting language, can also be used to implement recommendation algorithms. This article will introduce how to use PHP to implement a simple recommendation algorithm based on collaborative filtering, and provide corresponding code examples.

1. What is collaborative filtering algorithm
Collaborative filtering is a commonly used recommendation algorithm. It analyzes the common interests between users and recommends content that they may be interested in. The collaborative filtering algorithm finds other users who have similar hobbies to the current user based on the similarities between users, and then makes recommendations for the current user based on the evaluation of an item by these similar users. Collaborative filtering algorithms can be divided into two types:

1. User-based collaborative filtering (User-based Collaborative Filtering): uses the behavioral data of other users who have common interests with the current user to make recommendations.
2. Item-based Collaborative Filtering: Use the similarity between other items and the items that the current user is interested in to make recommendations.

This article will take the user-based collaborative filtering algorithm as an example to introduce how to use PHP to implement the recommendation algorithm.

2. Implementation steps

1. Collect user behavior data
   The recommendation algorithm needs to rely on user behavior data, such as user ratings of products, favorite movies, browsing records, etc. To simplify the example, we assume that there is already a user behavior data table, which contains fields such as user ID, item ID, and user's rating of the item.

2. Calculating the similarity between users
   Calculating the similarity between users is the core of the collaborative filtering algorithm. Commonly used similarity calculation methods include Euclidean distance, Pearson correlation coefficient, etc. Here we use Pearson correlation coefficient to calculate the similarity between users. The formula of Pearson correlation coefficient is as follows:

   similarity(u, v) = sum((r(u, i) - avg(u)) * (r(v, i) - avg(v))) / (sqrt(sum((r(u, i) - avg(u))^2)) * sqrt(sum((r(v, i) - avg(v))^2)))

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   where similarity(u, v) represents the similarity between users u and v, r(u, i) represents user u’s rating of item i, avg( u) represents the average rating of user u.

The following is an example of a function in PHP that calculates the Pearson correlation coefficient:

function pearson($ratings1, $ratings2) {
  $sum1 = $sum2 = $sumSq1 = $sumSq2 = $pSum = 0;
  $n = count($ratings1);

  foreach ($ratings1 as $item => $rating) {
    if (array_key_exists($item, $ratings2)) {
      $sum1 += $rating;
      $sum2 += $ratings2[$item];
      $sumSq1 += pow($rating, 2);
      $sumSq2 += pow($ratings2[$item], 2);
      $pSum += $rating * $ratings2[$item];
    }
  }

  $num = $pSum - ($sum1 * $sum2 / $n);
  $den = sqrt(($sumSq1 - pow($sum1, 2) / $n) * ($sumSq2 - pow($sum2, 2) / $n));

  if ($den == 0) return 0;

  return $num / $den;
}

This function receives two rating arrays as parameters and returns the difference between the two rating arrays The Pearson correlation coefficient.

3. Recommendations for users
   After calculating the similarity between users, we can make recommendations for the current user based on the similarity. The specific steps are as follows:
4. Traverse the user behavior data and find the k users most similar to the current user.
5. Based on the rating data of these k users, recommend items that the current user has not rated on them.

The following is an example of a function in PHP that recommends users:

function recommend($user, $data, $k) {
  $total = array();
  $simSum = array();

  foreach ($data as $otherUser => $ratings) {
    if ($otherUser != $user) {
      $similarity = pearson($data[$user], $ratings);

      if ($similarity > 0) {
        foreach ($ratings as $item => $rating) {
          if (!array_key_exists($item, $data[$user])) {
            if (!array_key_exists($item, $total)) {
              $total[$item] = 0;
              $simSum[$item] = 0;
            }

            $total[$item] += $rating * $similarity;
            $simSum[$item] += $similarity;
          }
        }
      }
    }
  }

  $recommendations = array();
  foreach ($total as $item => $score) {
    $recommendations[$item] = $score / $simSum[$item];
  }

  arsort($recommendations);
  return $recommendations;
}

This function receives the current user, user behavior data and the number of items to be recommended k as parameters, and returns an association The array represents the recommendation results, where the key is the item ID and the value is the recommendation score.

4. Summary
The collaborative filtering algorithm is a commonly used recommendation algorithm, which can achieve personalized recommendation content. This article introduces how to use PHP to implement a simple user-based collaborative filtering algorithm and provides corresponding code examples. Of course, many details and performance optimizations need to be considered in practical applications, such as processing large-scale data and preventing over-fitting. I hope this article can help you get started with the implementation of recommendation algorithms and provide some reference for further in-depth study.

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