PathFinder: Social Activity Job Matcher

PathFinder is a job recommendation web application that allows users to upload their social media activity documents (such as LinkedIn data exports in PDF format) and receive personalized job suggestions. The system leverages modern web technologies and natural language processing to match user profiles to relevant job roles using semantic similarity.

Tech Stack

Layer	Technology
Frontend	Next.js, React, Tailwind CSS, Axios
Backend	FastAPI, Python
NLP Model	Sentence Transformers (all-MiniLM-L6-v2)
Data	Pandas, Numpy, scikit-learn
PDF Parsing	PyMuPDF

Features

• Upload social media activity documents (PDF)
• Automatic text extraction and encoding
• Semantic similarity matching with precomputed job embeddings
• Top 5 job recommendations with similarity scores
• Responsive, modern UI
• Fetch live job details for the top recommendation using Apify API

Setup Instructions

Live Job Data Integration

PathFinder uses the Apify Indeed Scraper to fetch real-time job listings matching the top recommendation. To use this feature:

1. Create an account at Apify
2. Get your API token
3. Add the token to your backend .env file
4. The system will automatically fetch live job listings including:
   • Position details
   • Company information
   • Salary (when available)
   • Application links
   • Job type and location

Backend

1. Clone the repository

   git clone https://github.com/AchiraNadeeshan/social-activity-job-matcher.git
   cd social-activity-job-matcher/backend

2. Create and activate a virtual environment

   python -m venv venv
   .\venv\Scripts\Activate.ps1   # PowerShell
   # or
   .\venv\Scripts\activate.bat   # CMD
   # or
   source venv/bin/activate       # Unix/MacOS

3. Install dependencies

   pip install -r requirements.txt

4. Add your Apify API key Create a .env file in the backend directory:

   APIFY_API_TOKEN=your_apify_api_key_here
   

   Do not commit .env to version control.
5. Run the API

   uvicorn main:app --reload

   The API will be available at http://127.0.0.1:8000

Frontend

1. Install dependencies

   cd ../frontend
   npm install
   # or
   yarn install

2. Run the development server

   npm run dev
   # or
   yarn dev

   Visit http://localhost:3000

Recommendation Display Structure

• The top recommendation is displayed separately with live job details fetched from Apify.
• The next 4 recommendations are shown in a 2x2 grid below the top result.

Model Explanation

• Embedding Model: sentence-transformers/all-MiniLM-L6-v2
• Similarity Metric: Cosine Similarity
• Process:
  • User PDF is parsed and text extracted
  • Text is encoded into embeddings
  • Compared against precomputed job role embeddings
  • Top 5 jobs returned based on semantic similarity
  • Top job recommendation includes live job data fetched from Apify

Team Roles

Name	Registration No	Team Role
Achira Nadeeshan	22UG1-0723	Team Lead, Full Stack Development, Presentation, Documentation
H.M.K.S. Dedunupitiya	22UG1-0812	NLP Model, Presentation, Documentation
Pasindu Nimsara	22UG1-0627	Frontend Development, System Architecture Analysis
K.P. Dilhara	22UG1-0390	Documentation Lead
T. Siriwardhana	22UG1-0126	Data Scientist, System Architecture Analysis
H.L.V. Prasanjana	22UG1-0521	DevOps Engineer
U.V.C.T. Jayathilaka	22UG1-0380	Full Stack Developer
S.P.A.S. Senarathne	22UG1-0345	UI/UX Designer
Eranga Ekanayake	22UG1-0076	Research Analyst
Dewmina Bandara	22UG1-0804	QA Engineer

License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). You are free to use, share, and adapt the code for non-commercial purposes with proper attribution.

Contact

For issues or contributions, contact Achira Nadeeshan or open an issue on GitHub.