Impulse-ML: Recommender is PHP library which can be used to share personalized content for users on your website. It is written in PHP and requires no additional dependencies. With OOP API you can achieve good prediction results and you can quickly apply recommender system in any PHP application, e.g. in WordPress, Drupal or any other PHP framework based application.
Recommender system solves a machine learning problem. Given items (i.e. movies rated by user) are possible to rate by users (i.e. 0 – 5 star rating). With given rating data Recommender System can predict movie ratings, of those movies which are unrated by the user, find similar movies or even get the prediction for user who don’t rate any movie.
Impulse-ML: Recommender uses Collaborative Filtering algorithm so it is not required to provide item features, which can be understand as real item categories (i.e. comedy or action movie and their values) and it is not required to provide category features which can be understand as user preferences. The system learns by itself with only given items, categories and defined ratings.
As long as you set Learning Model parameters and Training parameters properly you might end up with pretty good prediction of rating the movie which is not rated by user yet – assuming that the more ratings you give the more accurate predictions you will get.
Impulse-ML: Recommender uses the gradient descent learning algorithm.
For general details about Recommender Systems you might consider visit https://en.wikipedia.org/wiki/Recommender_system to get intuition what is going on under the hood.
Check our official documentation and examples at https://impulse-ml.github.io/impulse-ml-recommender-php-documentation/