This repository contains code how to build job recommendation engine using Kaggle 'Job Recommendation Challenge' dataset

Aim is to come up with a job recommender system, which takes the skills from LinkedIn and jobs from Indeed and throws the best jobs available for you according to your skills.

Our solution for Recsys Challenge 2017.

Several baseline models and PJFNN on Job Recommendation Challenge

Job recommendation system using NLP, in which a user’s description is evaluated via a trained NLP model and jobs are suggested based on the similarities between the user’s skill set and the job’s required skill set. Jobs are scraped from various trustworthy sites in real time using Selenium and stored in a database.

This is base-line approach for building job recommendation engine

A simple job recommendation system project (using Python) for my final year!

Our extensions to KRED: Knowledge-Aware Document Representation for News Recommendations

DS307.N11 - Phân Tích Dữ Liệu Truyền Thông Xã Hội

One stop for Guided path, learning resources, projects, research and development and latest trends

[ICDM-2024] "DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation"

Revolutionizing Job Search with Personalized AI-Powered Recommendations

This job recommendation system helps connect candidates with suitable opportunities by analyzing skills. It combines data from Stack Overflow's 2018 Developer Survey and a Kaggle dataset.Improved job-candidate matching for a more efficient hiring process. Personalized recommendations based on skills and past successes.

This projects serves as a web app to showcase the recommendation engine project

Ajman Job Connect is a web app that streamlines job searches in Ajman. Users can input job titles via text or voice to get personalized recommendations. Powered by Flask, MongoDB, and machine learning, it offers an intuitive, efficient job search experience with real-time results and an easy-to-use interface.

Cette application recommande des offres d'emploi extraites de LinkedIn, Welcome to the Jungle, Indeed et Glassdoor, en fonction des compétences et des expériences des chercheurs d'emploi.

Job Recommendation with Content-Based Filtering & K-Nearest Neighbors.