UPGRADING FROM V1 TO V2

There are no special requirements for upgrading from v1 to v2, other than changing ^1.xx (xx can vary) to ^2.0 in your composer. json and running composer update. Of course, your app must meet the minimum requirements as well.

The supported way of installing phpjuice/opencf package is via Composer.

PHP Requirements

phpjuice/opencf Package requires PHP 7.4 or higher. If you are using an older version of php this package will not function correctly.

Older versions

If you do not meet the requirements, you can opt to use an older version of the package. We only actively maintain the latest version.

Find yourself stuck using the package? Found a bug? Do you have general questions or suggestions for improving the package? Feel free to create an issue on GitHub, we'll try to address it as soon as possible.

If you discover any security related issues, please email us at phpjuice@gmail.com instead of using the issue tracker.

All notable changes to opencf are documented on GitHub

PHP implementation of the (Weighted Slopeone,Cosine, Weighted Cosine) rating-based collaborative filtering schemes.

Usage

OpenCF Package is designed to be very simple and straightforward to use. All you have to do is:

1. Load a training set (dataset)

2. Predict future ratings using a recommender. (Weighted Slopeone,Cosine, Weighted Cosine)

Create Recommender Service

The OpenCF recommender service is created by direct instantiation:

use OpenCF\RecommenderService;

// Training Dataset
$dataset = [
    "squid" => [
        "user1" => 1,
        "user2" => 1,
        "user3" => 0.2,
    ],
    "cuttlefish" => [
        "user1" => 0.5,
        "user3" => 0.4,
        "user4" => 0.9,
    ],
    "octopus" => [
        "user1" => 0.2,
        "user2" => 0.5,
        "user3" => 1,
        "user4" => 0.4,
    ],
    "nautilus" => [
        "user2" => 0.2,
        "user3" => 0.4,
        "user4" => 0.5,
    ],
];

// Create a recommender service instance
$recommenderService = new RecommenderService($dataset);

// Retrieve a recommender (Weighted Slopeone)
$recommender = $recommenderService->weightedSlopeone();

// Predict future ratings
$results = $recommender->predict([
    "squid" => 0.4
]);

This should produce the following results:

[
  "cuttlefish" => 0.25,
  "octopus" => 0.23,
  "nautilus" => 0.1
];