If you’ve ever wanted to dive into the fascinating world of machine learning and recommendation systems, you’ve landed at the right spot! In this article, we’ll walk you through the process of creating a movie recommendation web application using Apache Spark machine learning, while also utilizing various IBM cloud technologies. So, grab your popcorn, and let’s get started!

Overview of the Project

This project is designed to guide you through creating a movie recommender application. You’ll find a series of Jupyter notebooks that can be run on IBM Data Science Experience. The excitement continues with a live demo you can interact with, featuring a robust movie recommendation system powered by Spark Streaming and IBM’s architecture.

Quick Start

Before diving deep, kick things off with an overview video on YouTube. This demo application is preloaded with around 4,000 movies and 500,000 ratings, allowing you to search for movies, rate them, and receive personalized recommendations based on your input.

Setting Up the Environment

Notebooks

Begin your journey with the Introduction Notebook, which offers insights into the project. To work with these notebooks, you may want to import them into IBM Data Science Experience. In case you face issues loading from a URL, don’t worry! You can clone or download the repository and import the notebooks as local files.

Technologies Used

The architecture of this application is built on several technologies:

• Core components (Web Application)
  • Python Flask for the web application
  • IBM Bluemix for hosting
  • IBM Cloudant NoSQL for data storage
  • IBM Data Science Experience (DSX) and Spark as a Service
• Optional components (Hadoop Warehouse)
  • IBM Compose Redis for Atomic Increment counter
  • IBM Message Hub for streaming ratings
  • IBM BigInsights for Spark Streaming

Setting Up Your Own Demo on Bluemix

Ready to deploy your own instance? Follow the instructions from the link below. Keep in mind this step might take about 30 minutes to complete:

CAUTION: This deployment includes a Python Flask application with 128MB memory and an instance of Cloudant Lite, potentially resulting in charges. Be sure to check your billing details before proceeding.

If you decide to incorporate optional components like Redis, Message Hub, or BigInsights, detailed instructions can be found here.

Building Recommendations

Once deployed, create a new DSX project and import the notebooks. Pay special attention to the [Step 07 Notebook](.notebooksStep%2007%20-%20Cloudant%20Datastore%20Recommender.ipynb), which focuses on generating and saving recommendations to Cloudant. Remember, you won’t see any recommendations until you’ve configured this notebook with your Cloudant credentials and executed it.

Web Application Screenshots

Rating a Movie

Here’s a glimpse into how users can rate movies using the application:

Movie Recommendations

Check out the movie recommendations generated by Spark machine learning:

Troubleshooting Guide

As you embark on this journey, you might encounter some hiccups along the way. Here are a few troubleshooting tips:

• Issue: Difficulty loading the notebooks from a URL.
• Solution: Consider cloning or downloading the repository and importing the notebooks directly.
• Issue: Not receiving recommendations after setting up the notebook.
• Solution: Ensure you have entered the correct Cloudant credentials in Step 07 and executed the notebook from DSX.
• Issue: Charges for IBM Bluemix services.
• Solution: Always double-check your billing and usage in the IBM Cloud dashboard to monitor your application’s cost.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.