Job Recommender System based on graphs

Ariam Bartsch, Arthur Cerveira, Carlos Calage

Introduction

This repository contains an implementation of a job recommender system based on graphs using the Neo4j database and Python. The system uses the Kaggle Job Recommendation Dataset and the relationships between the users and the jobs to recommend relevant jobs to the users.

Initial setup

To run the project, you must have Git, Python >= 3.6, and Docker installed on your machine.

First, you need to clone the repository to your local machine:

$ git clone https://github.com/arthurcerveira/Job-Recommender-System.git

Neo4j

Run the following command to start the Neo4j database with Docker:

$ docker run -p 7474:7474 \
             -p 7687:7687 \
             --env NEO4JLABS_PLUGINS='["apoc"]' \
             --env apoc.import.file.enabled=true \
             --env NEO4J_AUTH=neo4j/1234 \
             neo4j:4.2.2

Then, you can access the Neo4j browser sandbox at http://localhost:7474/browser/. The default username and password are neo4j and 1234, respectively.

Python

First you need to install the dependencies with pip:

$ pip install -r requirements.txt

Then you can load the data into the database:

$ python load_to_neo4j.py