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Job Recomendation System Using Knowledge Graph

Dealing with the enormous amount of recruiting information on the Internet, a job seeker always spends hours to find useful ones. To reduce this laborious work, we design and implement a recommendation system for online job-hunting. Instead of using CF algorithms we contrast on a Knowledge RS approach to figure out more interrelations between candidates and job description

Glimps of the Knowledge Graph

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Architectural Overview

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Open in the whimsical for better viewing experience

Dataset

Resume Dataset

The resume dataset is provided by “stack overflow” on the “Kaggle” website in 2018. Stack Overflow did a survey in which they asked the developer community about everything from their favorite technologies to their job preferences.

  • There are 98,855 responses in this public data release.
  • Dataset

Job Description Dataset

The job Description dataset was created by PromptCloud's in-house web-crawling service. This is a pre-crawled dataset, taken as a subset of a bigger dataset (more than 4.6 million job listings) that was created by extracting data from Dice, a prominent US-based technology job board in 2017.

  • There are 22,000 job profiles in this public data release.
  • Dataset

Testing and verifying results

Adamic Adar

  • Adamic Adar is a measure used to compute the closeness of nodes based on their shared neighbors.
  • The Adamic Adar algorithm was introduced in 2003 by Lada Adamic and Eytan Adar to predict links in a social network. It is computed using the following formula:where N(u) is the set of nodes adjacent to u.

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  • A value of 0 indicates that two nodes are not close, while higher values indicate nodes are closer.

  • The library contains a function to calculate closeness between two nodes.

Future Scope

  • Extending KG to more dimensions like location, salery
  • Using unstructured dataset
  • Native language support

FAQ's

1. What is the problem statement?

we are going to leverage a knowledge graph-based recommendation system that helps candidates to find jobs according to their skillsets.

2. What all are the general tasks?

We analysed various aspects which help to recommend job and job descriptions based on location, age group, etc. Future - Build homogenous graph's as in resume-skills, resume-location, resume-dev_type(backend/frontend), after that take the most popular nodes and build a heterogeneous knowledge graph

3. Why knowledge Graph?

A knowledge graph is self-descriptive, as it provides a single place to find the data and understand what it is all about. Knowledge graphs are being used for a wide range of applications from space, journalism, biomedicine to entertainment, network security, and pharmaceuticals.

4. Why Neo4j?

Neo4j delivers the lightning-fast read and write performance you need, while still protecting your data integrity.Neo4j graph algorithms are scalable and production-ready. Neo4j algorithms are written in Java and performance tested. NetworkX is a single node implementation of a graph written in Python. The response time is much faster in Neo4j.