Recommender-System-Comparision

About The Project

This project aims to compare various techniques used in implementing Recommender Systems based on their errors using Root Mean Square Error, Precision on top K and Spearman Rank Correlation.

(back to top)

Built With

• Python

(back to top)

Getting Started

To get a local copy up and running follow these simple example steps.

Installation

1. Clone the repository

   git clone https://github.com/shashwatanand1801/Recommender-System-Comparision.git

2. For preparing sparse npz matrix :

   cd src
   pyhton3 npzmaker.py

3. For the main python file :

   pyhton3 main.py

(back to top)

Results

(back to top)

Dataset

We used MovieLens 1M movie ratings dataset for our project. More details can be found here Movie ratings

(back to top)

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

(back to top)

Contact

Shashwat anand- @shashwat_anand - [email protected]

Project Link: https://github.com/shashwatanand1801/Recommender-System-Comparision

(back to top)