The SitePoint PHP blog has posted the next part in their series about using Predictive.IO and Lumen to create a simple movie recommendation application (part one is here). In this second part of the series they build on the environment created in the previous article and start developing the actual application.

In the intro, we covered the basics of PredictionIO and installed its dependencies. In this part, we’re going to build the movie recommendation app.

The tutorial starts with a brief configuration section to ensure you have your API keys configured correctly. Then it gets into the code:

• Pulling the data from the Movie DB API
• Creating the endpoint to perform the endpoint
• Picking random movies to show the user and recording their reactions (like/dislike)
• Creating the endpoint to recommend movies

Finally they share the configuration to set up the application deployment and train it with some example content you provide through some basic interactions. Finally they help you set up a cron job to train and redeploy the application every five minutes with the latest interaction information.

On the SitePoint PHP blog they've posted a tutorial showing you how to use the Prediction.IO server to create a movie recommendation application. Prediction.io is "an open source Machine Learning Server built on top of state-of-the-art open source stack for developers and data scientists create predictive engines for any machine learning task".

In this tutorial, I’m going to walk you through PredictionIO, an open-source machine learning server, which allows you to create applications that could do the following: recommend items (e.g. movies, products, food), predict user behavior, identify item similarity and rank items.

You can pretty much build any machine learning application with ease using PredictionIO. You don’t have to deal with numbers and algorithms and you can just concentrate on building the app itself.

The tutorial, the first part of a series, refreshes some older instructions for getting the Prediction.IO system up and running. He walks you through the creation of an AWS instance for the server a few different ways (Vagrant, Docker, etc). He then talks about the use of the Movie API from MovieDB and the two parts of the application that will be implemented on top of it: a learning phase and a recommendation phase. They show how to use Prediction.io to create the recommendation engine and make the new application on top of it. He helps you install some dependencies to use in the PHP side of the application and briefly explains what they're for.

This wraps up part one of the series. In the second part he starts putting this all to use and creates the PHP functionality to lay on top of the machine learning engine and handle learning and recommendations for users.

The SitePoint PHP blog continues their series about creating a movie prediction engine with Prediction.io in this second part focusing on implementation. In the first part of the series they set up the server and configuration to make the jump into the code. This second part gets more into the application side and features working code linking the prediction engine with the TheMovieDB API.

He jumps right into the code, showing how to:

• Fetch the data from the TMDB (via Flight and Guzzle)
• Populate the data back into the Prediction.io database
• Picking a random movie from the list (and outputting it to a page)
• Get movies the engine predicts as recommendations

The recommendations are based on ratings on other movies in the database with most of that logic happening behind the scenes instead of in the PHP script. The results are then output to the page along with the other movie data.

On the SitePoint PHP blog today Wern Ancheta has posted the first part of a series about creating a recommendation engine with the help of PHP and a system called Prediction IO.

In this tutorial, I'm going to walk you through Prediction IO, an open-source machine learning server. It allows you to create applications that could do the following: recommend items (e.g. movies, products, food), predict user behavior, identify item similarity and rank items. You can pretty much build any machine learning application with ease using Prediction IO. You don't have to deal with numbers and algorithms and you can just concentrate on building the app itself.

He walks you through the download and install of the Prediction IO software, how to start up the server and how to access its web interface. He shows you how to create an "engine" that will be used to make the recommendations and some of the settings allowing you to tailor it to your needs. The script will hook into The Movie DB API for content. He starts in on the PHP packages that will be needed to make the API connection and recommendations, but the actual code will come in a later article.