Best Job Recommendation System

Job Recommendation System

Finding the right job can be challenging, and connecting the right talent with the right opportunity is more important than ever. The Job Recommendation System Engine helps solve this problem by using data scraping, custom data processing, and machine learning to suggest jobs that best match a candidate’s skills.It works on a Collaborative-Based Recommendation System, focusing only on the skills mentioned in job postings and the skills the candidate has. This ensures that recommendations are accurate and relevant.The project is built as a Streamlit web application, making it simple to use and easy to adapt for real-world needs. It can be a useful tool for recruitment agencies, HR teams, and career platforms to improve the way they match candidates with jobs.

Overview

The system works on a custom-built dataset, created by merging the 2018 Stack Overflow Developer Survey data and a Kaggle job postings dataset. This combination ensures diversity in skill representation and job variety. The preprocessing pipeline uses custom feature extraction components to prepare the data for collaborative filtering.

By applying cosine similarity on the skills matrix, the engine predicts which job postings are the best fit for a given user profile. The similarity computation is optimized to handle large datasets, ensuring scalability.

Project Details

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Datasets Used

1. Stack Overflow Survey Dataset (2018) – Used to gather candidate skill sets, technologies, and experience details.
2. Kaggle Job Postings Dataset – Used to extract job descriptions, required skills, and posting details.

Both datasets are preprocessed and stored in CSV formats for fast retrieval. The processed data is available in the /data/collaborative filtering folder.

Feature Extraction & Preprocessing

A detailed Exploratory Data Analysis (EDA) is first performed to understand the data structure and relationships.

• Candidate skill features are extracted from Stack Overflow survey data.
• Job posting skills are extracted from Kaggle job data.
• The processed user and job profiles are stored as:
  • userskills.csv – Candidate skills mapping.
  • colabdata.csv – Job postings with skill attributes.

Additional profile datasets such as domain_user_profile.csv and languages_profile_job.csv are also generated for deeper analysis.

Collaborative Filtering Approach

The recommendation model is powered by a Collaborative Filtering algorithm:

• Cosine Similarity is computed between candidate profiles and job postings.
• Recommendations are based solely on skill matching — no external factors like location or salary are considered, ensuring unbiased recommendations.
• The similarity matrix is stored in similarity_matrix.pkl for reuse and rapid inference.

For testing purposes, recommendations for the first 200 users are stored in recommendations.csv.

Implementation Flow

1. Data Preparation
   • Place both datasets in /data/collaborative filtering.
   • Preprocess to generate feature files.
2. Model Execution
   • Run the collaborative filtering pipeline to compute similarity scores.
3. Output
   • Top 10 job recommendations per candidate are stored in recommendations.csv.
4. Frontend
   • The application frontend (app.py) is built with Streamlit.
   • Recommendations are displayed interactively, allowing easy navigation.

Available Features

• Custom Dataset Creation – Combines multiple datasets for enriched recommendations.
• Skill-Based Matching – Matches candidates and jobs purely on skill similarity.
• Preprocessed Data Storage – Optimized for fast loading and recommendations.
• Collaborative Filtering Model – Uses cosine similarity for accuracy.
• Streamlit Interface – Simple and professional UI for interacting with recommendations.
• Reusable Outputs – Precomputed recommendations and similarity matrix for scalability.

Professional Applications

This system can be integrated into:

• Recruitment Platforms – For automated skill-based job matching.
• HR Portals – To assist in internal candidate allocation.
• Career Counseling Tools – Providing targeted career recommendations.

It ensures faster candidate-job pairing, reduces recruitment time, and enhances candidate satisfaction by aligning opportunities with core skills.

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