Machine Learning


  • What is useful for
  • Available plugins
  • How to integrate
  • A little code

Applications

(mostly for text)

  • Anti-spam
  • Post recommendation
  • Post classification
  • Text analysis
  • Speech recognition
  • A/B testing
  • User login?

Available plugins

Integration

  • As a native plugin
  • As a wrapper for external services

Native

  • Everything inside your installation
  • Total control
  • No PHP machine learning libraries
  • Training in PHP is slow
  • CMS not good for training

External

  • Performance
  • No stress
  • Pay or maintain another system

Takeaway

  • Training/Algorithm (WP KO)

  • Model (WP OK)

  • Rest API is our friend

Let's write a model

  • Data: blog posts
  • Algorithm: text similarity
  • Model: graph stored in wp_postmeta
  • Recommend!

Algorithm

Jaccard/Tanimoto index
Jaccard/Tanimoto index

public static function similarity( $post_id_a, $post_id_b ) {

    // Transform each post in a bag of words
    $bow_a = self::post_2_bag_of_words( $post_id_a );
    $bow_b = self::post_2_bag_of_words( $post_id_b );

    // Compute Tanimoto
    $intersection = array_unique( array_intersect( $bow_a, $bow_b ) );
    $union        = array_unique( array_merge( $bow_a, $bow_b ) );

    return count( $intersection ) / count( $union );
}
Bag of Words

public static function post_2_bag_of_words( $post_id ) {
		
    $post = get_post( $post_id );

    // Take away HTML
    $post_content  = $post->post_content;
    $clean_content = strtolower( wp_strip_all_tags( $post_content ) );
    
    // Take away punctuation
    $clean_content = str_replace( array(".", ",", ":", ";", "!", "?", "'", '"', "(", ")" ), "", $clean_content );
    
    // Split tokens
    $tokens = array_unique( explode( " ", $clean_content ) );

    // Take away stopwords
    return array_diff( $tokens, self::$stopwords );
}

Similarity graph


function dropout_update_recommendations( $post_id ) {
	
	// Take latest 10 posts
	$latest_posts = wp_get_recent_posts(
		array(
			'numberposts' => 10,
			'post_type'   => array( 'post', 'page' )
		)
	);
	$similarities = array();
	
	foreach( $latest_posts as $another_post ){
		
		if( $another_post['ID'] !== $post_id ) {
			
			// Measure similarity
			$similarity = Dropout::similarity( $post_id, $another_post['ID'] );
			$similarities[ $another_post['ID'] ] = $similarity;

			// Update similarities for the other post
			$already_computed_similarities = get_post_meta( $another_post['ID'], 'dropout_recommendations', true );
			$already_computed_similarities[$post_id] = $similarity;
			update_post_meta( $another_post['ID'], 'dropout_recommendations', $already_computed_similarities );
		}
	}
	
	// Save recommendations for this post
	update_post_meta( $post_id, 'dropout_recommendations', $similarities );
}
add_action( 'save_post', 'dropout_update_recommendations' );
                        

function dropout_get_recommendations( $content ) {
	
	$recommendations = get_post_meta( get_the_ID(), 'dropout_recommendations', true );
	
	arsort( $recommendations );        // Sort by similarity

	$html = '
See also
    ';
	foreach( $recommendations as $recom_id => $similarity ) {
		$recom_post = get_post( $recom_id );
		$html .= '
  • ' . $recom_post->post_title . ' (' . $similarity .')
    • ';
	}
	$html .= '
';
	
	return $content . $html;
}
add_filter( 'the_content', 'dropout_get_recommendations' );
Show me the code!