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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.

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Built With

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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

npz sparse matrix

  1. For the main python file : 
    pyhton3 main.py

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Results

Results

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Dataset

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

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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

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Contact

Shashwat anand- @shashwat_anand - [email protected]

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

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