可用于向用户推荐的PHP库_PHP_下载.zip

# Recommend [](https://travis-ci.org/stojg/recommend) [](https://scrutinizer-ci.com/g/stojg/recommend/) [](https://scrutinizer-ci.com/g/stojg/recommend/) [](https://styleci.io/repos/15030893) This library should make it easier to find recommendations and similarities between different things. There are a couple of use cases that I developed it for: * Recommend a list of music albums/artists to a user * Recommend an article that is similar to the current one that a user is reading * Find other users that have the same values as another user (think matchmaking ;) ## Installation The easiest way to get this installed in your project is by using composer $ composer require stojg/recommend ## Usage Assuming that we have some data where users have rated music artists within a scale of one to five: $artistRatings = array( "Abe" => array( "Blues Traveler" => 3, "Broken Bells" => 2, "Norah Jones" => 4, "Phoenix" => 5, "Slightly Stoopid" => 1, "The Strokes" => 2, "Vampire Weekend" => 2 ), "Blair" => array( "Blues Traveler" => 2, "Broken Bells" => 3, "Deadmau5" => 4, "Phoenix" => 2, "Slightly Stoopid" => 3, "Vampire Weekend" => 3 ), "Clair" => array( "Blues Traveler" => 5, "Broken Bells" => 1, "Deadmau5" => 1, "Norah Jones" => 3, "Phoenix" => 5, "Slightly Stoopid" => 1 ) ); We then load this data into the Data class $data = new \stojg\recommend\Data($artistRatings); If we want to find artists that _Blair_ might like, we execute the recommend method. $recommendations = $data->recommend('Blair', new \stojg\recommend\strategy\Manhattan()); var_export($recommendations); The result of that computation would be: array ( 0 => array ( 'key' => 'Norah Jones', 'value' => 4, ), 1 => array ( 'key' => 'The Strokes', 'value' => 2, ) ) This means that _Blair_ might like `Norah Jones` and not like `The Strokes`. The `Recommender` works by finding someone in the `$artistRatings` that have rated artist similar to to _Blair_. In this case it turns out to be _Abe_, so it then tries to find artists that _Abe_ have rated but not _Blair_ and return them as a list of recommendations. How the 'nearest neighbour' is found depends on which strategy that is chosen and how big and dense the dataset is. ## The Dataset The general rule is that the bigger the dataset is, the better. It have to be formatted as an array in the following format: array( 'uniqueID' => array( 'objectID' => (int)'rating' ) ); Where in the case of the previous artist rating example * uniqueID = Blair * objectID = Music Artist * rating = an numeric value ## Strategies There are currently three strategies and which one to pick depends on how the data is organized and populated. ### Manhattan If the data is dense (almost all `objectID`s in the full data set have a non null rating) and the magnitude (rating) of the attributes values are important, this is a good strategy. I.e. all users have rated all music artists and they all agree on the same scale. ### Paerson Use this strategy if the data is dense but the ratings are subject to grade-inflation. I.e. if user A have rated all artists between 2-4 and user B have rated artists between 4-5 this strategy tries to compensate for the fact that the user A’s rating of 2 is equal to Users B’s 4. ### Cosine This is the strategy to pick if the data is sparse. I.e. If there is a list with ten thousand artists, it quite likely that the users only listened and rated a few of them. ## Articles There is a provided helper class for recommending articles that are similar to another article. The implementation is quite stupid, but it should give you a hint on how to expand this library with your own datasets. ### Usage $articleData = new \stojg\recommend\ArticleData(); $allArticles = getFromDatabase(); foreach($allArticles as $article) { $articleData->push($article->id, $article->content); } $recommendedArticle = $articleData->recommend($articleID = 4);