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Code for the paper 'Making personnel selection smarter through wordembeddings: A graph-based approach'

This repository hosts code for the papers:

image1

Datasets

Available in this link

Test Results

Edit experiments.ipynb.

Installation

Prequisites:

  • Windows 10 64-bit / Debian based Linux 64-bit.
  • Python 3 (min. version 3.6), pip3 (& py launcher Windows-only).
  • Working Neo4j Database (min. version 4.1.2).

Windows 10

Download the project from the green button above, unzip it,
and then open a cmd terminal to this folder and type pip3 install -r requirements.txt.
This command will install the neccessary Python libraries* to run the project.

Debian Based Linux

We ran the following commands to update Python, git,
clone the project to a local folder and install the necessary Python libraries*.

sudo apt install python3.6
sudo apt install git-all
git clone https://github.com/imis-lab/personnel-selection
cd personnel-selection
pip3 install -r requirements.txt

* Optionally you could create a virtual environment first,
* to isolate the libraries from your python user install.
* However the setup script doesn't downgrade existing libraries,
* so there's zero risk in affecting your local user install.

Database Setup (Windows / Linux)

Create a new database from the Neo4j desktop app using 4.1.2 as the min. version.
Update your memory settings to match the following values,
and install the following extra plugins as depicted in the image. image2 Hint: if you use a dedicated server that only runs Neo4j, you could increase these values, accordingly as specified in the comments of these parameters.

Run the GraphOfDocs_Representation.py script which will create thousands of nodes, and millions of relationships in the database.
Once it's done, the database is initialized and ready for use.

Running the app

You could use the Neo4j Browser to run your queries, or for large queries you could use the custom visualization tool
visualize.html which is located in the GraphOfDocs_Representation Subdirectory.

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