JobSync is a self-hosted, open-source job application tracker and AI-powered career assistant. Built with Next.js and Shadcn UI, it helps job seekers manage their search journey with AI resume review, job matching, task logging, and application analytics—all while keeping your data private.

你的求职AI co-pilot，优化你的项目，定制你的简历，为你匹配工作，并帮助你做好面试准备。此外，还提供一个免费的面经导向的面向offer学习网站：https://pinkprisma.com Your AI job-hunting co-pilot, optimizing your projects, customizing your resume, matching you with jobs, and helping you prepare for interviews.

AI-powered Python script for automated resume-job matching with scoring, PDF generation, and personalized email responses.

The purpose of this project was to defeat the current Application Tracking System used by most of the organization to filter out resumes. In order to achieve this goal I had to come up with a universal score which can help the applicant understand the current status of the match. The following steps were undertaken for this project 1) Job Descriptions were collected from Glass Door Web Site using Selenium as other scrappers failed 2) PDF resume parsing using PDF Miner 3) Creating a vector representation of each Job Description - Used word2Vec to create the vector in 300-dimensional vector space with each document represented as a list of word vectors 4) Given each word its required weights to counter few Job Description specific words to be dealt with - Used TFIDF score to get the word weights. 5) Important skill related words were given higher weights and overall mean of each Job description was obtained using the product for word vector and its TFIDF scores 6) Cosine Similarity was used get the similarities of the Job Description and the Resume 7) Various Natural Language Processing Techniques were identified to suggest on the improvements in the resume that could help increase the match score

AI-powered job matching agent using CoreSpeed's Zypher framework. Analyzes JD-resume fit, finds matching jobs, and provides actionable insights.

The idea is to calculate the similarity between the resume and the job description and then return the resumes with the highest similarity score.

According to a 2015 study on job seeking behavior by Pew Research Center, 79% of the job seekers utilized the online resources for their most recent employment (Aaron ,2015). This study result suggests that the online job boards become the major channel for job seekers in the digital era. However, another finding in the study indicates that most of the job seekers fail to match their experiences with the job requirements and spend hours on job board to apply job which is not seen to be suitable (Aaron, 2015). Additionally, Dr. John Sullivan conducted a similar research in 2013 which highlighted some interesting aspects: on average, 250 resumes are received for each job opening by the major organizations, more than 50% of the resumes does not meet the minimum requirement (John, 2013). This means the time our recruiter spends on these 50% of the resumes for each job is wasted. From both candidate and recruiter’s points of view, the phenomenon may suggest that the traditional online job board does not seem to simplify the job application process or reduce the effort required from both parties. With this challenge getting bigger and bigger, the demand to automate the resume - job matching process is getting increased as well. For instance, the content - based recommendation system (CBR) is introduced to analyze the job description to identify the potential area of interest to the job seekers (Shiqiang et al., 2016). To apply the concept in Singapore local context, our team has conducted a text mining project based on the data acquired from the major online job board in Singapore. The primary objective of this project is to create a machine learning model to accelerate the job - resume matching process. The detail of the text mining methodology and results are presented in the following sections.

a machine learned solution with rich features for job matching

A must have tool for job seekers and recruiters. This project is intended to find and recommend the best fit. Job seekers can find best matching jobs to their resume and Recruiters find the best fit resumes for any job posting. Its based on Machine learning "NLP" concepts of text content match via Doc2Vec and similarity scores.

A streamlined platform for efficient resume-job matching using advanced NLP techniques and a user-friendly web interface, built with Flask and MongoDB.

Extracting details from Resume(CVs) and matching with Job Description(JDs) using pretrained model like DistilBERT and ranking them using cosine similarity.

A CNN + Glove deep learning approach to predict job titles on Indeed job data with 0 feature engineering needed

AI application to find matching jobs based on your resume from custom job boards

Resume matching tool using Gemini AI for job descriptions and resumes 📄

A project that uses NLP to extract skills and degree from resumes and job postings. The results are matched using the jaccard index to come up with a matching score which is used to come up with job recommendations based on an individuals resume.

Resume optimizing and job matching app

No description available

The official Java SDK for the Textkernel Tx v10 API. Includes a resume parser (CV parser), job parser, resume search engine, and resume matching engine.

The official C# SDK for the Textkernel Tx v10 API. Includes a resume parser (CV parser), job parser, resume search engine, and resume matching engine.

An AI-powered Career Navigation Platform that guides a user through their entire career lifecycle: Resume → Skill Gap → Learning Path → Career Simulation → Job Matching → Application Support The system acts as a personal AI career mentor, adapting to each user’s profile instead of forcing them into predefined paths . (Enter "API KEY" in dashboard

Automated resume matching - NLP & ML used to extract info from PDF resumes, tokenize text, calculate cosine similarity scores, and rank top matches for job descriptions.

ResuMatch is a full-stack web application designed to streamline the job application process for job seekers. It offers features like resume analysis, intelligent job matching, an interactive interview preparation chatbot, and a customizable resume builder and get job suggestions. Built with React, Django, and Tailwind CSS.

Upload your resume and check out your best matching jobs!

Dataset for the paper 'Interview Choice Reveals Your Preference on the Market: To Improve Job-Resume Matching through Profiling Memories'

A CV is a representation of a candidate’s profile, skill sets and achievements. Matching CV across a job description to identify potential candidates to interview is a tedious job. In this project we will be solving this problem by designing and implementing resume parser and analyzer which will help recruiters to process resumes by extracting data in a meaningful way given a certain job description. The application is aimed for recruiters to efficiently manage electronic resume documents sent via the internet.

Automated Resume Screening System (With Dataset) A web app to help employers by analysing resumes and CVs, surfacing candidates that best match the position and filtering out those who don't. Description Used recommendation engine techniques such as Collaborative , Content-Based filtering for fuzzy matching job description with multiple resumes. Prerequisites Software textract==1.6.3 requests==2.22.0 Flask==1.1.1 gensim==3.8.0 sklearn==0.0 PyPDF2==1.26.0 autocorrect==0.4.4 nltk==3.4.5 contractions==0.0.21 textsearch==0.0.17 inflect==2.1.0 numpy==1.17.2 pdfminer.six==20181108 Python 3.6.0 |Anaconda 4.3.0 (64-bit)| Dataset Link1 : https://s3.ap-south-1.amazonaws.com/codebyte-bucket/Resume%26Job_Descriptions.zip Mirror : https://drive.google.com/open?id=17M9oDPip5JFFFNJhDCBQKy8BMqoyxajU Running localhost Run pip install -r requirements.txt to install dependencies Simply run this command from root directory. Login Username :admin Password : whitetigers2018 python app.py Running using Docker Create the docker image running: docker build -t arss . Run the container: docker run -it -p 5000:5000 arss

Jobox is a website that makes it easy for job seekers to find the best jobs based on their resume or CV. We provide job recommendations by matching applicants' skills and job descriptions using artificial intelligence.

AI-powered job matching system that automatically fetches jobs from LinkedIn & SEEK, matches them against your resume using Google's Gemini API, and enriches results with Glassdoor company insights.

ATS Catalyst is an innovative platform designed to empower job seekers and streamline the hiring process. Leveraging advanced AI technologies, our suite of tools offers personalized feedback, resume evaluation, and job description matching to enhance your job application process.

AI-powered resume analyzer that scores resumes, provides ATS feedback, and offers job-matching suggestions. Built with React, TypeScript, Zustand, and Puter.js for AI, auth, and storage.