Code for the paper 'Making personnel selection smarter through wordembeddings: A graph-based approach'
This repository hosts code for the papers:
- - Download
Available in this link
Edit experiments.ipynb
.
Prequisites:
Windows 10
64-bit / Debian basedLinux
64-bit.Python 3
(min. version 3.6),pip3
(&py
launcher Windows-only).- Working
Neo4j
Database (min. version 4.1.2).
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.
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.
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.
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.
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.
- Nikolaos Giarelis ([email protected])
- Nikos Kanakaris ([email protected])
- Nikos Karacapilidis ([email protected])