How to implement a recommendation system in PHP?

As the number of Internet users continues to increase, recommendation systems have become one of the core technologies necessary for many Internet companies. By analyzing user behavior and interests, the recommendation system can recommend relevant products, content, etc. to users, improve user satisfaction and loyalty, and increase the company's revenue. In this article, we will focus on how to implement a recommendation system in PHP.

1. The basic principle of the recommendation system

The basic principle of the recommendation system is to use the user’s historical behavior and personal information to recommend recommendations that the user may be interested in through algorithm analysis and mining. H. The most commonly used algorithms in recommendation systems include collaborative filtering, content-based recommendations, and hybrid recommendations. Below we briefly introduce the principles of these algorithms.

1.1 Collaborative filtering

Collaborative filtering is one of the most commonly used algorithms in recommendation systems. The basic idea is based on user similarity, that is, if two users liked similar items in their past behavior patterns, they may also like similar items in their future behavior patterns. Therefore, collaborative filtering will predict the user's evaluation or preference for an item by analyzing the similarities between users to achieve recommendations.

1.2 Content-based recommendation

Content-based recommendation is to recommend items similar to those that have been browsed through the analysis of the user's historical behavior and personal information. This algorithm is based on the similarity between items rather than the similarity between users. The basic idea is to establish a similarity relationship between items based on the characteristics of the items and the user's historical behavior, thereby recommending resources that may be of interest to the user.

1.3 Hybrid recommendation

Hybrid recommendation combines multiple algorithms and comprehensively utilizes the advantages of different algorithms to improve the accuracy and credibility of recommendations. Hybrid recommendation can be achieved through weighted average, Boosting, Stacking and other technologies.

2. Methods to implement recommendation systems in PHP

2.1 Data preprocessing

The most critical step in the recommendation system is data preprocessing. Data preprocessing refers to cleaning, transforming and standardizing raw data to facilitate the application of subsequent algorithms. Data preprocessing mainly includes the following steps:

(1) Data cleaning: remove duplicate values, outliers, missing values ​​and other useless data.

(2) Data conversion: Convert original data into a format that the computer can process, such as converting text into numerical types, converting discrete data into continuous data, etc.

(3) Data standardization: Standardize the data, such as scaling all data to the range of [0,1] or [-1,1], so that different data can be effectively compared.

2.2 Implementation of collaborative filtering algorithm

The collaborative filtering algorithm is one of the more commonly used algorithms in recommendation systems. In PHP, we can use user-based collaborative filtering algorithm to implement the recommendation system. The steps are as follows:

(1) Calculate the similarity between users

First, you need to calculate the similarity between users. Methods such as Pearson correlation coefficient and cosine similarity can be used to calculate the similarity between users. The Pearson correlation coefficient can be implemented with the following code:

function sim_pearson($prefs, $p1, $p2) {
$si = array();
foreach ($prefs[$p1] as $item => $value) {

if (array_key_exists($item, $prefs[$p2]))
  $si[$item] = 1;

}
$n = count($si);
if ($n == 0) return 0;

$sum1 = $sum2 = $sum1Sq = $sum2Sq = $pSum = 0;
foreach ($si as $item => $value) {

$sum1 += $prefs[$p1][$item];
$sum2 += $prefs[$p2][$item];
$sum1Sq += pow($prefs[$p1][$item], 2);
$sum2Sq += pow($prefs[$p2][$item], 2);
$pSum += $prefs[$p1][$item] * $prefs[$p2][$item];

}

$num = $pSum - ($sum1 * $sum2 / $n);
$den = sqrt(($sum1Sq - pow($sum1, 2) / $n) * ($sum2Sq - pow($sum2, 2) / $n));
if ($den == 0) return 0;
return $num / $den;
}

Among them, $prefs stores user rating data Arrays, $p1 and $p2 are the user numbers whose similarity is to be calculated.

(2) Calculate the recommendation results

The weighted average method or the nearest neighbor algorithm can be used to calculate the recommendation results. The weighted average method refers to calculating the recommendation result by calculating the weighted average of the ratings of all similar users, with similarity as the weight; the nearest neighbor method refers to finding the users with the highest similarity to the target user, and then using their average as the recommendation result. .

2.3 Implementation of content-based recommendation algorithm

The content-based recommendation algorithm mainly recommends based on the similarity of items. In PHP, the following process can be used to implement a content-based recommendation algorithm:

(1) Calculate the similarity between items

To calculate the similarity between items, you can use cosine similarity or Jaccard similarity. Taking cosine similarity as an example, it can be achieved through the following code:

function sim_cosine($prefs, $p1, $p2) {
$sum1Sq = $sum2Sq = $pSum = 0;
foreach ($prefs[$p1] as $item => $value) {

$sum1Sq += pow($value, 2);
if (array_key_exists($item, $prefs[$p2]))
  $pSum += $value * $prefs[$p2][$item];

}

foreach ($prefs[$p2] as $item => $value) {

$sum2Sq += pow($value, 2);

}

if ($sum1Sq * $sum2Sq == 0) return 0;
return $pSum / sqrt($sum1Sq * $sum2Sq);
}

Among them, $prefs is an array storing item characteristics, $p1 and $p2 are the item numbers for which similarity is to be calculated.

(2) Calculate recommendation results

When calculating recommendation results, the nearest neighbor algorithm or weighted average method can be used. The weighted average method refers to the weighted average of the scores of items that are similar to the target item, and the similarity is used as the weight; while the nearest neighbor algorithm refers to selecting the items with the highest similarity to the target item, and then taking the average value as the prediction result.

3. Summary

The recommendation system is a necessary core technology for Internet companies, which can help companies improve user satisfaction and loyalty and increase revenue. In PHP, we can use algorithms such as collaborative filtering and content-based recommendation to implement recommendation systems. During implementation, it is necessary to perform data preprocessing, calculate the similarity between items or users, and then calculate the recommendation results through weighted average or based on the nearest neighbor algorithm. Finally, the recommendation system needs to be continuously optimized to improve its accuracy and practicality.

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