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64 lines
2.0 KiB
PHP
64 lines
2.0 KiB
PHP
<?php
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/**
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* Jingga
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*
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* PHP Version 8.1
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*
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* @package phpOMS\Business\Recommendation
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* @copyright Dennis Eichhorn
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* @license OMS License 2.0
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* @version 1.0.0
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* @link https://jingga.app
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*/
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declare(strict_types=1);
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namespace phpOMS\Business\Recommendation;
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use phpOMS\Math\Matrix\Matrix;
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/**
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* Model based collaborative filtering
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*
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* @package phpOMS\Business\Recommendation
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* @license OMS License 2.0
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* @link https://jingga.app
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* @see https://realpython.com/build-recommendation-engine-collaborative-filtering/
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* @since 1.0.0
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*/
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final class ModelCF
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{
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/**
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* Constructor
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*
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* @since 1.0.0
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* @codeCoverageIgnore
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*/
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private function __construct()
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{
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}
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/**
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* Calculate the score of a user <-> item match.
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*
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* This function calculates how much a user likes a certain item (product, movie etc.)
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*
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* $user and $item can also be Vectors resulting in a individual evaluation
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* e.g. the user matrix contains a user in every row, every column represents a score for a certain attribute
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* the item matrix contains in every row a score for how much it belongs to a certain attribute. Each column represents an item.
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* example: users columns define how much a user likes a certain movie genre and the item rows define how much this movie belongs to a certain genre.
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* the multiplication gives a score of how much the user may like that movie.
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* A segnificant amount of attributes are required to calculate a good match
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*
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* @param Matrix $users A mxa matrix where each "m" defines how much the user likes a certain attribute type and "a" defines different users
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* @param Matrix $items A bxm matrix where each "b" defines a item and "m" defines how much it belongs to a certain attribute type
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*
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* @return array
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*
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* @since 1.0.0
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*/
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public static function score(Matrix $users, Matrix $items) : array
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{
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return $users->mult($items)->getMatrix();
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}
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}
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