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a full-stack web app that suggests weekly research papers to subscribers based on specified interests

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ScholarLink

Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Contact

About The Project

ScholarLink is a web application that provides personalized weekly research paper recommendations to subscribed users based on their specified interests.

Features

  • Subscribe
  • Unsubscribe
  • Provide interests
  • Fetch previous interests
  • Subscribers will receive weekly research paper recommendations via email, tailored to their interests

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Built With

  • React, Node, Express, MongoDB, JavaScript, Python, RabbitMQ, Docker, Springer API

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System Design

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Getting Started

Prerequisites

  1. Get a free API Key at https://dev.springernature.com/ for Instruction section 2.recommender
  2. Follow the instructions to get the credentials for SMTP transporter with Nodemailer for Instruction section 2.mail

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Instruction

  1. Clone the repo

    git clone https://github.com/Hsin-Hung/ScholarLink.git
  2. Set up the environment variables by creating an .env file in each of the following directories with the specified keys:

    • recommender
          API_KEY=<your Springer API key>
      
    • mail
          MAIL_USERNAME=<your email username>
          MAIL_PASSWORD=<your email password>
          OAUTH_CLIENTID=<your oauth cliend id>
          OAUTH_CLIENT_SECRET=<your oauth client secret>
          OAUTH_REFRESH_TOKEN=<your oauth refresh token>
      
  3. Configurations (Optional):

        // Set to "*/30 * * * * *" to schedule mail every 30 secs for testing
        CRON_SCHEDULE: "0 17 * * 5" // Default schedules weekly mail every 17:00 Fri
  4. Run and access the web app through port 8080

    docker compose up

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Contributing

Contributions to this project are welcome. If you find a bug or have an idea for a new feature, please open an issue or submit a pull request.

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License

Distributed under Apache-2.0 license. See LICENSE for more information.

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Contact

Henry - [email protected]

Hackathon Finalists: HackSharks

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a full-stack web app that suggests weekly research papers to subscribers based on specified interests

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