I don't know if we will ever implement some ranking algorithm here but here are some few things I learns about algorithm and would like to share it with @evg maybe you can look into it and give me your feedback

So I designed this simple algorithm in PHP

```
<?php
// Function to calculate the ranking score for an article
function calculateRankingScore($article, $userPreferences)
{
// Assign weights to different factors
$likesWeight = 0.4;
$commentsWeight = 0.3;
$bookmarksWeight = 0.2;
$sharesWeight = 0.1;
// Get current timestamp
$currentTimestamp = time();
// Calculate the age of the article in seconds
$ageInSeconds = $currentTimestamp - strtotime($article['timestamp']);
// Calculate the time decay factor based on the age of the article
$timeDecayFactor = 1 / log10(max($ageInSeconds, 1) / 3600 + 2);
// Calculate the user interest factor based on their reading preferences
$userInterestFactor = getUserInterestFactor($article['topic'], $userPreferences);
// Calculate the final ranking score
$rankingScore = ($likesWeight * $article['likes']) +
($commentsWeight * $article['comments']) +
($bookmarksWeight * $article['bookmarks']) +
($sharesWeight * $article['shares']);
// Apply time decay and user interest factors
$rankingScore *= $timeDecayFactor * $userInterestFactor;
return $rankingScore;
}
// Function to get the user's interest factor for a specific topic
function getUserInterestFactor($topic, $userPreferences)
{
// Check if the user has a preference for the topic
if (isset($userPreferences[$topic])) {
// Get the user's interest factor for the topic
$interestFactor = $userPreferences[$topic];
} else {
// Default interest factor if no preference is found
$interestFactor = 0.5;
}
return $interestFactor;
}
// Example articles array
$articles = [
[
'id' => 1,
'likes' => 100,
'comments' => 50,
'bookmarks' => 30,
'shares' => 20,
'timestamp' => '2023-05-24 10:00:00',
'topic' => 'technology'
],
[
'id' => 2,
'likes' => 50,
'comments' => 20,
'bookmarks' => 10,
'shares' => 5,
'timestamp' => '2023-05-25 08:00:00',
'topic' => 'science'
],
// Add more articles here...
];
// User preferences (example)
$userPreferences = [
'technology' => 0.8,
'science' => 0.6,
'health' => 0.4
];
// Calculate ranking scores for all articles
foreach ($articles as &$article) {
$article['rankingScore'] = calculateRankingScore($article, $userPreferences);
}
// Sort the articles based on their ranking scores (in descending order)
usort($articles, function ($a, $b) {
return $b['rankingScore'] - $a['rankingScore'];
});
// Print the sorted articles
foreach ($articles as $article) {
echo 'Article ID: ' . $article['id'] . ' - Ranking Score: ' . $article['rankingScore'] . "\n";
}
```

Here is how it works

Weighted Factors: The algorithm assigns weights to different user interactions, such as likes, comments, bookmarks, and shares. These weights reflect the relative importance of each interaction in determining the ranking score. Adjust the weights according to your preferences to prioritize certain interactions over others.

Time Decay: The algorithm calculates the age of each article in seconds by comparing the current timestamp with the article's timestamp. It then applies a time decay factor to account for the freshness of the article. Older articles receive a lower time decay factor, which reduces their impact on the ranking score. The specific formula used here applies a logarithmic decay based on the age of the article.

User Interest Factor: The algorithm considers the user's reading preferences for different topics. It retrieves the user's interest factor for a specific topic from their preferences. If no preference is found, a default interest factor is used. The interest factor reflects how interested the user is in articles related to a particular topic. You can assign interest factors to topics based on user behavior or explicitly captured preferences.

Ranking Score Calculation: The algorithm calculates the ranking score for each article based on the weighted factors, time decay, and user interest factor. The ranking score is the sum of the weighted user interactions, multiplied by the time decay factor and the user interest factor.

Sorting: After calculating the ranking scores for all articles, the algorithm sorts the articles in descending order based on their ranking scores. This ensures that articles with higher scores appear at the top of the list.

Output: The algorithm then prints the sorted articles, displaying their ID and corresponding ranking score.

^{+ 97}