This project aims to build a recommendation system using MXNet models and AWS SageMaker Factorization Machines Algorithm. We will be using the Movielens dataset, which is a popular movie rating dataset containing a large number of ratings from users for movies. We will build a model that predicts the rating a user would give a movie based on their past ratings and preferences.
To run this project, you will need the following:
- Python 3.6 or later
- AWS account
- AWS SageMaker instance
- Movielens dataset
Clone this repository to your local machine.
git clone https://github.com/Raghul-G2002/Recommendation-System-FM.git
- Download the Movielens dataset from here.
- Upload the dataset to your AWS S3 bucket.
- Open the recommendation_system.ipynb notebook using Jupyter Notebook on your SageMaker instance.
- Update the BUCKET_NAME variable to your S3 bucket name.
- Follow the instructions in the notebook to train and deploy the recommendation model.
This project is based on the work of AWS Samples and Apache MXNet.
This project is licensed under the MIT License - see the LICENSE file for details.