Recommendations for PHP using collaborative filtering
MIT License
🔥 Recommendations for PHP using collaborative filtering
Run:
composer require ankane/disco
Add scripts to composer.json
to download the shared library:
"scripts": {
"post-install-cmd": "Disco\\Library::check",
"post-update-cmd": "Disco\\Library::check"
}
And run:
composer install
Create a recommender
use Disco\Recommender;
$recommender = new Recommender();
If users rate items directly, this is known as explicit feedback. Fit the recommender with:
$recommender->fit([
['user_id' => 1, 'item_id' => 1, 'rating' => 5],
['user_id' => 2, 'item_id' => 1, 'rating' => 3]
]);
IDs can be integers or strings
If users don’t rate items directly (for instance, they’re purchasing items or reading posts), this is known as implicit feedback. Leave out the rating.
$recommender->fit([
['user_id' => 1, 'item_id' => 1],
['user_id' => 2, 'item_id' => 1]
]);
Each
user_id
/item_id
combination should only appear once
Get user-based recommendations - “users like you also liked”
$recommender->userRecs($userId);
Get item-based recommendations - “users who liked this item also liked”
$recommender->itemRecs($itemId);
Use the count
option to specify the number of recommendations (default is 5)
$recommender->userRecs($userId, count: 3);
Get predicted ratings for specific users and items
$recommender->predict([['user_id' => 1, 'item_id' => 2], ['user_id' => 2, 'item_id' => 4]]);
Get similar users
$recommender->similarUsers($userId);
Load the data
use Disco\Data;
$data = Data::loadMovieLens();
Create a recommender and get similar movies
$recommender = new Recommender(factors: 20);
$recommender->fit($data);
$recommender->itemRecs('Star Wars (1977)');
Save recommendations to your database.
Alternatively, you can store only the factors and use a library like pgvector-php. See an example.
Disco uses high-performance matrix factorization.
Specify the number of factors and epochs
new Recommender(factors: 8, epochs: 20);
If recommendations look off, trying changing factors
. The default is 8, but 3 could be good for some applications and 300 good for others.
Pass a validation set with:
$recommender->fit($data, validationSet: $validationSet);
Collaborative filtering suffers from the cold start problem. It’s unable to make good recommendations without data on a user or item, which is problematic for new users and items.
$recommender->userRecs($newUserId); // returns empty array
There are a number of ways to deal with this, but here are some common ones:
Get ids
$recommender->userIds();
$recommender->itemIds();
Get the global mean
$recommender->globalMean();
Get factors
$recommender->userFactors($userId);
$recommender->itemFactors($itemId);
Thanks to LIBMF for providing high performance matrix factorization
View the changelog
Everyone is encouraged to help improve this project. Here are a few ways you can help:
To get started with development:
git clone https://github.com/ankane/disco-php.git
cd disco-php
composer install
composer test