bcc/myrrix-edition

There is no license information available for the latest version (dev-master) of this package.

The "Myrrix Edition"

dev-master / 2.3.x-dev 2013-05-03 14:10 UTC

Requires

• php: >=5.4
• bcc/myrrix-bundle: *
• doctrine/doctrine-bundle: 1.2.*
• doctrine/orm: >=2.2.3,<2.4-dev
• jms/di-extra-bundle: 1.3.*
• jms/security-extra-bundle: 1.4.*
• sensio/distribution-bundle: 2.2.*
• sensio/framework-extra-bundle: 2.2.*
• sensio/generator-bundle: 2.2.*
• symfony/assetic-bundle: 2.1.*
• symfony/monolog-bundle: 2.2.*
• symfony/swiftmailer-bundle: 2.2.*
• symfony/symfony: 2.2.*
• twig/extensions: 1.0.*

Unknown License

• Symfony2
• myrrix
• mahout
• hadoop
• recommendation

README

Welcome to the Myrrix demo for Symfony2. It relies on the Symfony2 standard edition, adding BCCMyrrixBundle, and a tweaked version of the AcmeBundle that runs an example of recommender engine based on the MovieLens dataset.

1. Whats inside

• Symfony2 standard edition 2.2 RC1 (http://symfony.com/)
• Myrrix serving layer 0.9 (http://myrrix.com/)
• The MovieLens dataset 1M (http://www.grouplens.org/node/73)
• A AcmeDemoBundle working as a recommending website

1. Running the example

You first need to have composer installed:

curl -s http://getcomposer.org/installer | php

First get the code. You can download it or get it via composer:

php composer.phar create-project bcc/myrrix-edition path/to/install

Start myrrix using this command line:

java -Dmodel.features=18 -jar myrrix.jar --localInputDir app/cache/myrrix --port 84

Then install the dependencies:

php composer.phar install

You then need to setup your database (you can change any default configuration in the app/config/parameters.yml file):

php app/console doctrine:database:create
php app/console doctrine:schema:create

Then you have to load all the data of the MovieLens dataset into your database and into the myrrix recommender engine:

php app/console --env=prod acme:demo:load-data-set

This will take a very long time, it will output a message every 1000 inserted rows, there are 1 000 000 items to load.

You can now navigate into the root url and test the recommender engine.