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

amiradridi/Job-Resume-Matching

Folders and files, repository files navigation, job resume matching project.

Matching the best profiles to a job description may be a difficult and time-consuming task. In fact, the traditional way that recruiters are using to select candidates doesn't take into consideration all important details. Recruiters have to screen all the applications manually and then calculate the similarity in an efficient way.

1st step: information retrieval

Information Extraction is the task of automatically extracting structured information such as entities, relationships between entities, and attributes describing entities from unstructured sources. Our system uses spacy PhraseMatcher to extract the information from job descriptions. We prepared a dictionary that has all education degrees categories, all majors and skills categories related to computer engineering field. We fed that dictionary to the Spacy rule-based PhraseMatcher in order to detect and recognize entities in our job description. The job information extraction would look like:

Our structured job descriptions dataset :

2nd step: Matching rules

We implemented matching rules to calculate the similarity between the resume and the job description. Those matching rules don't only use simple keywords matching but also ontology matching techniques.

* Education section matching rules

* Majors section matching rules

* Skills section matching rules

In this part, we will use semantic similarity-based approach to match resumes' skills and jobs' skills. Semantic similarity approach is the task of searching for documents or sentences (resumes) which contain semantically similar content to a search document (the job description).

We tried different pre-trained models to embed our words ('all-mpnet-base-v2', 'paraphrase-MiniLM-L6-v2', 'gpt3','all-MiniLM-L12-v1','all-roberta-large-v1','bert-base-nli-mean-tokens'). Embeddings are a key part of modern NLP, they encode the meaning of words or other linguistic units into vectors of numbers.

The steps that we followed later on to calculate the similarity between the job and the resume with each model:

We derive semantically meaningful word embeddings using the different models mentioned above

We compare those embeddings with cosine similarity to find the nearest resumes to the job description Cosine similarity is defined as the inner product of two vectors divided by the product of their length. Cosine similarity is defined as:

We tried that approach on skills words of 15 resumes and 5 jobs : The resumes information were extracted by a colleague and the jobs were extracted from a public dataset on Kaggle.

These are some of the scores' results:

These are the ranks of resumes for each job:

Evaluation:

We used precision @k metric to evaluate our models' results comparing to manually annotated dataset. Precision at k is the proportion of recommended items in the top-k set that are relevant. In our case k is 15. These are the results for each job and model:

The total precision for each model on 5 jobs:

Visit this notebook for the full code of the semantic similarity approach.

• 3rd step: We Calculated the final similarity score (the mean of skills matching score, degrees matching score and majors matching score) and returned the resumes with the highest similarity score.

Project summary

• We retrieved information from the job description using Spacy rule-based PhraseMatcher
• We implemented matching rules for the degrees' levels and the acceptable majors
• We compared between 6 powerful word embedding models to generate the skills matching scores and we chose 'bert-base-nli-mean-tokens' for its high precision

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks paper

Sbert pretrained models official documentation

Sentence embeddings documentation

• Python 83.1%
• Cython 1.7%

DEV Community

Posted on Jul 21, 2023 • Updated on Dec 4, 2023

Creating a Game-Changer in Job Search: An Open Source ATS Resume Matcher

Hello 👋, Dev Community! I have created an open-source ATS (Applicant Tracking System) tool called "Resume Matcher." This project aims to assist job seekers in making it past that challenging initial resume screening process.

• Website (Made in Astro): www.resumematcher.fyi
• GitHub: Resume Matcher
• Demo on Streamlit: Resume Matcher
• Discord for discussion .

Resume Matcher

Srbhr / resume-matcher, resume matcher is an open source, free tool to improve your resume. it works by using language models to compare and rank resumes with job descriptions..

𝙹𝚘𝚒𝚗 𝙳𝚒𝚜𝚌𝚘𝚛𝚍 ✦ 𝚆𝚎𝚋𝚜𝚒𝚝𝚎 ✦ 𝙳𝚎𝚖𝚘 ✦ 𝙷𝚘𝚠 𝚝𝚘 𝙸𝚗𝚜𝚝𝚊𝚕𝚕 ✦ 𝙲𝚘𝚗𝚝𝚛𝚒𝚋𝚞𝚝𝚎 ✦ 𝙳𝚘𝚗𝚊𝚝𝚎 ✦ 𝚃𝚠𝚒𝚝𝚝𝚎𝚛

Resume Matcher is an AI Based Free & Open Source Tool. To tailor your resume to a job description. Find the matching keywords, improve the readability and gain deep insights into your resume.

Upvote us on ProductHunt 🚀 .

Don't let your resume be a roadblock from getting your next job. Use Resume Matcher!

How does it work?

The Resume Matcher takes your resume and job descriptions as input, parses them using Python, and mimics the functionalities of an ATS, providing you with insights and suggestions to make your resume ATS-friendly.

The process is as follows:

Parsing : The system uses Python to parse both your resume and the provided job description, just like an ATS would.

Keyword Extraction : The tool uses advanced machine learning algorithms to extract the most relevant…

The Standard Process

We all know the drill 📝. You spend time creating your resume, applying for the job, and then... no replies. Why is that? The answer often lies in the automated screening systems called Applicant Tracking Systems (ATS). These systems use algorithms to scan resumes for specific keywords and criteria. If your resume doesn't hit the right notes, it may never see human eyes, no matter how qualified you are.

Why use Resume Matcher

That's where the " Resume Matcher " comes in. This Python-based project serves as an aid to check if your resume is ATS-friendly or not. It's designed to analyze your resume against the job description you're applying for. But that's not all! The tool uses Spacy, NLTK, Vector Databases, and semantic similarity to highlight common keywords between the job description and your resume. Provide keyword analysis, matching keywords between the job description and your resume. While providing common key terms in your resume as well. Along with a vector similarity score.

So, you get an idea of which extra keywords you can include and where you can improve.

Why I'm creating this

I have faced the same challenges before. I've applied to multiple jobs, and in some instances, I had the right experience. But I never got any call or interview opportunity 😞. This made me realize there's more to job hunting than your skills. Your resume has to include the keywords in the job description, and it should be able to be parsed by ATS well. That's where the idea of Resume Matcher came in. Now, with many LLMs, NLP, and Machine Learning Algorithms, I can create a good resume. But this project still has a long way to go. And I need the community's help; I need help to do all the proper work and people to guide me well. And someone who understands the pain of applying and never getting a callback can also relate to this.

What's the current status, and what I'm looking for?

Currently, this project only has a Streamlit app for demo. If the web developers can create a dashboard for it where people can upload their resumes, then the Python developers can do their magic on the backend. It'll be a great help 🙏.

Development areas:

• Web Development (creating a react dashboard)
• Python Backend (Fast Api, Flask, Django, etc.)
• Someone who understands LangChain, Vector Databases, and LLMs can aid in creating a prompt with the discovered keywords so people can use them.
• Data Visualization.
• Improve the Readme , provide documentation, etc.
• Improve the landing page website from Astro to react.

It's for you

My aim is to help the people in the tough times. There have been many layoffs, and people are still looking for jobs. Many young people graduates can also be supported by this project. Also, for those who contribute, this project can be a really lovely addition to their resumes and their GitHub as well.

Once again 🔗 Links:

If you liked this post, please support the project. You can find me on GitHub @srbhr

Top comments (23)

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• Education Community College
• Work Freelance
• Joined Nov 13, 2021

It looks like a good project!! I have forked it. Will try to contribute.

• Joined Jun 24, 2023

Cool I will like to help you with the dashboard, the idea is really amazing

• Email [email protected]
• Location India
• Education Computer Science (Bachelors Degree)
• Pronouns He/Him
• Work Software Developer, Dev Rel
• Joined Sep 25, 2019

@nonfungiblehayor | Thanks, you can join in the discord and ping me. And we can create a discussion on GitHub as well on how we can approach this. Yes, if implemented consider how many people can be helped. Especially, in layoffs, or while searching for a new job. Career change and for graduates.

You give to the community, you'll get good stuff in return.

Kindly drop the discord link or tag so we can discuss

Yes there you go: github.com/srbhr/Resume-Matcher Discord: discord.gg/t3Y9HEuV34

• Email [email protected]
• Location Lagos,Nigeria
• Work Software Engineer | Technical Writer | Artificial Intelligence Enthusiasts
• Joined Mar 25, 2021

Hello, is there any documentation? Would love to improve that.

@shegz We need a documentation to get started. There isn't any but you can create one and put up a pull request.

Is there a way I can reach out to you directly. Like whatsApp or twitter. Thanks.

You can reach out to me on discord. Link: discord.gg/t3Y9HEuV34

Alright, will do so. Thanks.

• Education FEFU 2011, but mostly self-educated
• Work Freelancing since 2013
• Joined Nov 4, 2021

Dude, I'm front-end specialist with some UX/UI background. I'd like to contribute to your project. Link me to some outstanding issues.

Job hunting is even a more pain in the ass in 2023, who woulda thought that all that work experience would mean so little. Let's do something about it.

• Joined Jul 22, 2023

Looks cool, will try it!

• Joined Jul 26, 2023

I am not yet a developer but how can we contribute with donations?

Thanks, if you will see the GitHub Repo: github.com/srbhr/Resume-Matcher There is a sponsor button over there. You can sponsor there. Or check this section: github.com/srbhr/Resume-Matcher#su...

• Location Zagreb, Croatia
• Joined Apr 24, 2019

This can be a nice Saas project

Thanks! Maybe in the coming few months, we can have a Saas offering.

I'm going to dive deep into the code and see if there's any room for some tweaks or improvements. By the way, tool is really awesome!

• Location Jalandhar,Punjab
• Work STUDENT
• Joined Oct 14, 2020

Really insigtful @srbhr

• Location Kitchener, Canada
• Education University of Pretoria, Conestoga College
• Pronouns She / Her
• Work Web developer at Studio Locale
• Joined Dec 8, 2021

This is a really great idea. I will try to contribute if my time allows for it.

Thanks @annetawamono you can join the Discord: discord.gg/t3Y9HEuV34

• Joined Dec 8, 2019

forked, will see if there is any contribution to the project

Yes Alex, there are a lot of things to improve in this project. If you want to have a discussion please join the discord. discord.gg/t3Y9HEuV34

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Your Tool for Tailoring Resumes to Job Descriptions Resume Matcher

Resume Matcher is your secret weapon in the job market. Our free, open-source ATS tool empowers you to tailor your resume perfectly for each job you apply. Join us 🚀 in our journey to build a free, open-source ATS tool powered with Machine Learning.

Our Mission

Our goal is to empower developers with a deeper understanding of their resumes and skill sets. By comparing your resume to a range of job descriptions, we highlight the similarities and differences, enabling you to make informed, data-driven decisions.

Natural Language Processing

We leverage state-of-the-art natural language processing techniques to extract and comprehend the context of resumes and job descriptions. This technology allows us to provide you with precise and meaningful insights.

Vector Similarity - Upcoming Feature

In our continuous effort to enhance your experience, we are planning to incorporate Weaviate Vector Database, Qdrant Vector Search, and Milvus support into our project. These additions will further refine our ability to match your resume with job descriptions.

Data Visualization

We believe in the power of data. Our platform generates comprehensive data visualizations, enabling you to make decisions based on concrete information rather than mere heuristics.

User-Friendly Interface

We understand the importance of a seamless user experience. Our web page is designed to be intuitive and user-friendly, allowing you to interact with the project with ease.

Text Similarity

Our text similarity feature provides a detailed comparison between your resume and job descriptions. This feature helps you identify the areas where your resume aligns with the job requirements and where it needs improvement.

Python-Based

Our project is currently written in Python, a powerful and versatile programming language known for its readability and efficiency.

Open to ideas

Join the Innovation

Resume Matcher, a free open-source project, is built by developers for developers. We aim to simplify resume creation and invite innovative ideas to accelerate job searches. Your unique contributions are always welcome!

With the rise of LLMs, generating resumes has become significantly easier. If you have expertise in this area, you could help us integrate LLMs into our platform.

A streamlined, intuitive dashboard can greatly enhance the user experience. If you're skilled in front-end development, particularly with frameworks like React or Vue, you could help us create a more user-friendly interface.

Docker images can simplify the deployment process, making it easier for users to try and use our project. If you're familiar with Docker, you could help us create a Docker image for ResumeMatcher.

Data visualization is key to helping users understand the insights we provide. If you have a knack for creating clear, compelling data visualizations, your skills could greatly enhance our platform.

Our text similarity feature is central to our tool. If you have expertise in natural language processing or machine learning, you could help us refine our text similarity algorithms, making our insights even more precise and valuable.

To make our tool accessible to as many users as possible, we aim to support multiple languages. If you're multilingual and have experience in localization, you could help us expand our language support.

Good documentation is crucial for any open-source project. If you have a knack for clear, concise writing, you could help us improve our documentation. This would make it easier for new contributors to understand our project and get involved.

The user interface and user experience (UI/UX) are key to making our tool accessible and enjoyable to use. If you have skills in UI/UX design, you could help us make our landing page and overall platform more intuitive and visually appealing.

We welcome all ideas that can enhance Resume Matcher and help users land their dream jobs faster. Join us in revolutionizing the job search process!

Resume Matcher

Ready to take your job search to the next level 🚀 Give Resume Matcher a try today and see the difference it can make!

🚀 Open Source ATS Tool with Streamlit "Resume Matcher" powered by Vector Search

Best, Saurabh Rai

Hi @srbhr ,

Thanks for sharing your project! Looks good!

This topic was automatically closed 180 days after the last reply. New replies are no longer allowed.

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• SEMANTIC SIMILARITY
• SEMANTIC TEXTUAL SIMILARITY

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26 Jul 2016  ·  Yiou Lin , Hang Lei , Prince Clement Addo , Xiaoyu Li · Edit social preview

Job search through online matching engines nowadays are very prominent and beneficial to both job seekers and employers. But the solutions of traditional engines without understanding the semantic meanings of different resumes have not kept pace with the incredible changes in machine learning techniques and computing capability. These solutions are usually driven by manual rules and predefined weights of keywords which lead to an inefficient and frustrating search experience. To this end, we present a machine learned solution with rich features and deep learning methods. Our solution includes three configurable modules that can be plugged with little restrictions. Namely, unsupervised feature extraction, base classifiers training and ensemble method learning. In our solution, rather than using manual rules, machine learned methods to automatically detect the semantic similarity of positions are proposed. Then four competitive "shallow" estimators and "deep" estimators are selected. Finally, ensemble methods to bag these estimators and aggregate their individual predictions to form a final prediction are verified. Experimental results of over 47 thousand resumes show that our solution can significantly improve the predication precision current position, salary, educational background and company scale.

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📄 Matching Resumes to Job Specifications

How I used NLP to help efficiently place prospective candidates

I always jump at the opportunity to tackle new and interesting challenges using Machine Learning and Advanced Analytics. In the constantly changing field of AI, this is how I discover and push the bounds of what is truly possible.

1. The problem

In the fast-paced world of job recruitment, staying ahead of the curve requires innovative solutions. Recently, I was tasked with probably my toughest challenges by a client who operates a job recruitment firm within South Africa. I undertook the challenge of revolutionizing the client’s current recruitment process by harnessing the power of advanced Natural Language Processing (NLP) techniques.

In this blog post, I’ll take you through the intricacies of the project, detailing the architecture, preprocessing pipelines, data storage, APIs, trained models, user interface, and the intricate AI logic that powers the system.

2. Understanding the clients needs

Understanding the client’s needs was the crucial first step in the journey. The client’s existing recruitment process was time-consuming and inefficient, relying heavily on its recruiters to manually review each resume and match it to relevant job openings. This resulted in slower placement times and potential missed opportunities for both job seekers and employers.

To address these challenges, the client sought a solution that could not only streamline their current processes by automating the matching of resumes to job specifications but also bring innovation to the forefront of their operations. They wanted a system that could accurately identify relevant resumes for each open position (and vice versa), eliminating the need for manual screening.

Before developing the solution, I spent time understanding the specific needs of the client. I met with the company’s recruiters to understand their key pain points and how they currently matched resumes to job openings. I also analyzed the company’s data to identify any patterns or trends that could be used to improve the matching process. Through this collaborative process, I gained crucial insights into the intricacies of their current recruitment workflow, enabling myself to tailor my solution to their specific needs. As a result, I developed a set of requirements for the proposed solution:

• Automated : The solution should be able to automatically match resumes to job openings (and vice versa) without any manual intervention.
• Accurate : The solution should be able to accurately match resumes to job openings (and vice versa) based on the skills, experience, and qualifications of the candidates.
• Scalable : The solution should be able to handle a large volume of resumes and job openings.
• Easy to use : The solution should be easy to use for JobCrystal’s recruiters.

3. The solution architecture

The success of the project relied heavily on creating a well-designed solution architecture. The system’s core comprises multiple components collaborating to provide a strong and intelligent recruitment solution. Each stage, from data ingestion to storing results in ElasticSearch, was crafted with emphasis on scalability, efficiency, and smooth integration into the client’s current infrastructure. Figure 1 visually depicts the interconnected components, showcasing a rough architecture of the solution.

3.1. Data initialisation

To kickstart the process, the system ingests data from two primary sources: The client’s FTP server and Dropbox account. The FTP server contains approximately 35 000 unlabelled documents, while Dropbox contributes around 3 000 labeled resumes associated with job specifications. This data undergoes an initialization step, whereby raw text is extracted from the documents, cleaned (using the clean-text package ) and stored as separate sentences in an Elasticsearch database. Additionally, five metadata extraction steps are executed at this stage which encompass updating information related to file locations, industry and job title labels, and constructing sets of named entities.

3.2. Training the models

The system utilized various trained models to perform specific tasks associated with document preprocessing and unsupervised ranking. Two Bidirectional Encoder Representations from Transformers (BERT) models are finetuned in order to predict, given the raw resume text, a candidate’s industry and associated job title, giving rise to the IndustryBERT and JobTitleBERT models, respectively. The fine-tuned models ensure accurate categorization, forming the foundation for subsequent stages in the process.

A transformer-based spaCy Named Entity Recognition (NER) model is also trained at this stage to extract key information like key skills, location, degrees and college names which may be used for downstream filtering. Additionally, Uniform Manifold Approximation and Projection (UMAP) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) models are fitted to facilitate document vector representations which is an essential component in the subsequent unsupervised ranking step.

3.3. Preprocessing the data

All sentences comprising the raw resume text are then fed through various preprocessing steps, the outputs of which are each stored in separate indices in Elasticsearch. During this phase, the finetuned industry and job title BERT models as well as the custom NER model are leveraged, together with two pre-trained BERT-based models. More specifically:

• Each resume sentence is classified according to the most likely industry and associated job title using the IndustryBERT and JobTitleBERT models.
• Resume-specific named entities are extracted from the resume using the custom spaCy NER model.
• Each resume sentence is converted to a vector representation using the well-known SentenceTransformers package.
• Each resume sentence is classified according to what part of a resume the sentence likely describes (e.g. experience, education, skills, certifications, awards, hobbies, references, etc.) using a pretrained model .

3.4. Data storage

Elasticsearch served as the central repository for storing preprocessed document data. Furthermore, the ability to execute database queries according to the cosine similarity measure makes it an extremely efficient database for enabling efficient document retrieval. A dedicated AWS EC2 instance is used to host the Elasticsearch instance.

3.5. The “AI logic”

The AI logic is the heart of the system, orchestrating the matchmaking process. By considering predicted industry and job titles, user-specified keyword entities, and leveraging unsupervised ranking techniques, our system intelligently filters and ranks documents, providing personalized recommendations to users. This is accomplished through 5 sequential steps facilitated by a Streamlit user interface displayed in Figure 2.

• Step 1. The first step requires the user to select whether they are wanting to match a resume to a job specification.
• Step 2. The user may then upload the resume to the system in their PDF or DOCX format. Uploading a resume triggers the initialisation and prepocessing procedures described in §3.1 and §3.3, the results of which are also stored in the Elasticsearch database. During this step, the industry and corresponding job title of the candidate are predicted using the IndustryBERT and JobTitleBERT models desribed in §3.2, respectively.
• Step 3. The predicted industry and corresponding job title of the candidate are then surfaced to the user as a confirmation. If an errorous prediction was made by the model, the user has the opportunity to correct the classification at this stage. This step is important since the candidate’s suitability will only be assessed according to those job specifications matching the candidate’s industry and job title.
• Step 4 In some cases, the user may wish to further filter out the suitability of the candidate to a job specification unless specific entities are not present in both documents. More specifically, the user may specify in this step that specific skills, degrees, colleges and/or histroic company names must be present in both documents. This is accomplished by utilising our custom spaCy NER model described in §3.2.
• The first algorithm relies on ordering all candidate job specifications by utilising Elasticsearch’s text similarity functionality . More specifically, job specifications are ordered according to the cosign similarity between the text vector representations of the job specifications according to the resume’s vector representation.
• The second algorithm takes inspiration from the recently proposed Top2Vec approach and utilises the pre-fitted UMAP and HDBSCAN models described in §3.2. First, the spaCy document vector representation of the resume is reduced from 300 to 80 dimensions using the UMAP model. This is done since UMAP has been shown to be an effective preprocessing step to boost the performance of density based clustering methods. This reduced vector representation then acts as input to the HDBSCAN model which produces a cluster prediction. The most appropriate job specifications to recommend are those which share the same cluster as the resume.

The effectiveness of the resume matching system was extensively evaluated by the client using the Streamlit user interface.

After thoughrough evaluation of the system, the client was impressed with the overall quality of the recommendations and I proceeded to productionising the solution.

4. Wrapping up

By leveraging advanced NLP techniques, I was able to create a system that significantly improved the client’s efficiency and effectiveness in finding the right candidates for their open positions, as confirmed by the positive client feedback in Figure 3. The client was so impressed with the solution that they decided to reposition their business offering around this new AI-based recruitment matching system, as shown in the accompanying video below.

As NLP continues to evolve, I can only expect even more innovative applications in the field of recruitment, further transforming the way companies find and hire top talent. It’s projects like this that futher cement my belief that Data Science and Machine Learning has the power to revolutionise almost every industry.

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Computer Science > Computation and Language

Title: competence-level prediction and resume & job description matching using context-aware transformer models.

Abstract: This paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable candidates. A total of 6,492 resumes are extracted from 24,933 job applications for 252 positions designated into four levels of experience for Clinical Research Coordinators (CRC). Each resume is manually annotated to its most appropriate CRC position by experts through several rounds of triple annotation to establish guidelines. As a result, a high Kappa score of 61% is achieved for inter-annotator agreement. Given this dataset, novel transformer-based classification models are developed for two tasks: the first task takes a resume and classifies it to a CRC level (T1), and the second task takes both a resume and a job description to apply and predicts if the application is suited to the job T2. Our best models using section encoding and multi-head attention decoding give results of 73.3% to T1 and 79.2% to T2. Our analysis shows that the prediction errors are mostly made among adjacent CRC levels, which are hard for even experts to distinguish, implying the practical value of our models in real HR platforms.

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4 Resume Mistakes to Avoid When You Don’t Have Much Experience

• Irina Cozma

A good resume can set you apart and help you land that interview.

Understanding the common mistakes job candidates make on resumes, and how to overcome them, can set you apart from your competitors. The first mistake is including irrelevant work experience. Instead, only add roles that are relevant to the position you’re applying for. The second is customizing your resume. While it’s in your benefit to adjust your resume to better match the job description, over-tuning your resume for every application can be a waste of time — and end up slowing down your search. This is especially true if you’re focused on securing a particular position that has a standard job title like “marketing coordinator” or “sales associate.” The third is overdesigning your resume. Recruiters only spend a few seconds scanning it, so keep the format simple and straightforward. The fourth is coming off as a novice. For example, don’t use an ancient email address — update it to something that sounds more professional, and give your resume a specific name so it’s easier to identify.

Resumes. Love them or hate them, you will probably need one when you apply for a job. The resume has a specific tactical role to play in your search — to get you the interview. You need to make sure it checks a few boxes to do that work because, even if you take advantage of your network, sooner or later, you will need to share your resume with the hiring manager.

• Irina Cozma , Ph.D., is a career and executive coach who supports professionals to have better career adventures. She coached hundreds of Fortune 500 executives from global organizations like Salesforce, Hitachi, and Abbott. Irina also coaches startups and the Physicians MBA at the University of Tennessee. Download her free career guide to help you prepare for your next career adventure.

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