Create a Movie Recommendation App with Prediction.io - Setup

This tutorial guides you through building a movie recommendation app using the open-source machine learning server, PredictionIO. This powerful tool simplifies the creation of applications capable of recommending items, predicting user behavior, and identifying item similarities. Let's dive into the process.

Key Concepts:

• PredictionIO is an open-source machine learning server ideal for building recommendation engines and predictive applications. It handles the complex algorithms, allowing you to focus on application development.
• Setting up PredictionIO requires Java (Version 6 or higher) and MongoDB. After installation, access the web interface at port 9000 to create users and applications.
• The movie recommendation engine within PredictionIO is highly customizable, allowing you to define item types, training schedules (using Cron expressions), recommendation preferences (freshness, serendipity, etc.), and the recommendation goal.
• The application itself will involve two phases: a learning phase where users rate movies, and a recommendation phase where the app suggests movies based on user input. We'll use the MovieDB API to fetch movie data.

Installation and Setup:

This tutorial assumes a Linux environment. For other operating systems, consider using Homestead Improved for a streamlined setup.

1. Download PredictionIO and extract it to your desired location.
2. Install prerequisites (Java 6 and MongoDB) using the provided setup script: bin/setup-vendors.sh
3. Start PredictionIO: bin/start-all.sh
4. Access the web interface at http://localhost:9000. Create a user account using bin/users.
5. Create a new app (e.g., "movie_recommendations") and note the App Key.

6. Add an item recommendation engine (e.g., "movie-recommender").

7. Configure the engine settings:
   • Item Types: Leave as default (all item types).
   • Training Schedule: Set to 0 * * * * ? for minute-by-minute training (Cron expression).
   • Recommendation Preferences: Adjust freshness, serendipity, unseen items only, seen actions, and number of recommendations to your liking. For this example, we'll use "Recommend any items."
   • Recommendation Goal: Set to "like."

MovieDB API Integration:

Obtain an API key from The Movie Database (TMDB) to access their movie data.

Application Development:

The app will consist of a learning phase (users rate movies) and a recommendation phase (the app suggests movies).

1. Install Dependencies: Add these to your composer.json:

<code class="language-json">{
  "require": {
     "damel/flight-skeleton": "dev-master",
     "predictionio/predictionio": "~0.6.0",
     "guzzlehttp/guzzle": "4.*"
  },
  "autoload": {
    "classmap": [
      "controllers"
    ]
  }
}</code>

Run composer install and composer dump-autoload.

2. Create .htaccess: Add the following to ensure proper routing:

<code class="language-apache">RewriteEngine On
RewriteCond %{REQUEST_FILENAME} !-f
RewriteCond %{REQUEST_FILENAME} !-d
RewriteRule ^(.*)$ index.php [QSA,L]</code>

(Use appropriate Nginx configuration if needed.)

3. Develop the App: Create an index.php file to handle routing and app initialization. Develop controllers to handle the learning and recommendation phases, using the PredictionIO PHP SDK and Guzzle to interact with PredictionIO and the MovieDB API respectively.

(Learning Phase Example)

(Recommendation Phase Example)

Conclusion:

This tutorial provides a foundation for building a movie recommendation app with PredictionIO. The next steps involve implementing the detailed application logic, connecting to the MovieDB API, and creating the user interface. Remember to handle error conditions and optimize for performance.

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