Scholarly Recommender

End-to-end product that sources recent academic publications and prepares a feed of recommended readings in seconds.
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

As an upcoming data scientist with a strong passion for deep learning, I am always looking for new technologies and methodologies. Naturally, I spend a considerable amount of time researching and reading new publications to accomplish this. However, over 14,000 academic papers are published every day on average, making it extremely tedious to continuously source papers relevant to my interests. My primary motivation for creating ScholarlyRecommender is to address this, creating a fully automated and personalized system that prepares a feed of academic papers relevant to me. This feed is prepared on demand, through a completley abstracted streamlit web interface, or sent directly to my email on a timed basis. This project was designed to be scalable and adaptable, and can be very easily adapted not only to your own interests, but become a fully automated, self improving newsletter. Details on how to use this system, the methods used for retieval and ranking, along with future plans and features planned or in development currently are listed below.

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Built With

• [![Pandas][Pandas.org]][Pandas-url]

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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

• npm

  npm install npm@latest -g

Installation

1. Get a free API Key at https://example.com
2. Clone the repo

   git clone https://github.com/iansnyder333/ScholarlyRecommender.git

3. Install NPM packages

   npm install

4. Enter your API in config.js

   const API_KEY = 'ENTER YOUR API';

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Usage

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

For more examples, please refer to the Documentation

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Roadmap

• Feature 1
• Feature 2
• Feature 3
  • Nested Feature

See the open issues for a full list of proposed features (and known issues).

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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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License

Not Distributed under the MIT License. Don't See LICENSE.txt for more information.

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Contact

Ian Snyder - @iansnydes - [email protected]

Project Email - [email protected]

My Website: iansnyder333.github.io/frontend/

Linkedin: www.linkedin.com/in/ian-snyder-aa1600182/

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Acknowledgments

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[Pandas.org](https://img.shields.io/badge/Pandas [Pandas-url]:https://pandas.pydata.org/