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recommender_system_research_papers_or_scholarly_articles_doc2vec

Recommender system for Scholarly Articles Dataset available at https://www.kaggle.com/neelshah18/arxivdataset and text file for topics explanation is copied from https://arxiv.org/archive/cs


Site is live on http://deviantpadam.pythonanywhere.com/
Instructions to run the flask app -
  1. Install git
  2. Open cmd/terminal
  3. Clone this repository using git clone https://github.com/DeviantPadam/rec_system.git or download the zip file.
  4. Install required dependencies from requirement.txt using pip install package_name or conda install -c
  5. Open cmd/terminal and set current directory to repository directory using cd repository_path
  6. Execute export FLASK_APP=recommender.py(linux) or set FLASK_APP=recommender.py (windows)
  7. Then execute flask run
  8. Click on the development link to see the flask app

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Recommender system and search engine for scholarly articles/research papers using Doc2Vec

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  • CSS 60.4%
  • HTML 22.3%
  • Python 11.9%
  • JavaScript 5.4%