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The matching algortithm between candidate and job description for our workproject at NOVA SBE

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NOVA SBE workproject matching_algo

This Repo contains the matching algortithm between candidate and job description for our workproject at NOVA SBE

Broad overview of the workflow and pipeline:

  • Datasets: {job descriptions, CVs}
  • Extract relevant skills using Spacy PhraseMatcher etc.
  • Matching rules (big job – dependent on which datasets we are using)
  • Embeddings (skill2Vec, BERT, etc.)
  • Calculate similarity scores and rankings

Next steps:

  1. Try to train a double encoder transformer architecture on synthetic data
  2. Fix database unique identifier issues

CAVEATS:

  • Bias in models
  • Tailor interview questions to the pool of applicants
  • Measure of performance (e.g. labelled datasets)
  • Productivity data for employees

Setting up the conda venv

Create the environment with the config-file

conda env create -f environment.yml

Conda should now have set you up with all the necessary dependencies to run the project. Please activate the environment:

conda activate workproject

You could also enable a jupyter-kernel if you want to use this environment in jupyter notebooks:

conda python -m ipykernel install --user --name workproject --display-name "workproject"

Usage

To run the flask application locally on your machine (first navigate to the user_interface directory)

flask run

(or for debug mode)

python app.py

Acknowledgements

Big thanks to Amira for letting us use her code as a building block for our project:

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