https://github.com/alphasf9/job-recommendation-portal-2k21-project

A Job portal made using MERN stack technologies
https://github.com/alphasf9/job-recommendation-portal-2k21-project

cloudinary express mongodb multer nodejs reactjs

Last synced: 21 days ago
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A Job portal made using MERN stack technologies

• Host: GitHub
• URL: https://github.com/alphasf9/job-recommendation-portal-2k21-project
• Owner: Alphasf9
• Created: 2024-10-13T07:26:08.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-10-23T19:25:37.000Z (23 days ago)
• Last Synced: 2024-10-24T06:04:07.904Z (22 days ago)
• Topics: cloudinary, express, mongodb, multer, nodejs, reactjs
• Language: JavaScript
• Homepage:
• Size: 3.26 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Job-Recommendation-Portal-2K21-Project

A job recommendation portal built using the MERN stack (MongoDB, Express, React, Node.js) involves various components working together to provide a seamless experience for users seeking jobs and employers posting job listings. Here’s a breakdown of key elements involved:

### 1. **Frontend (React)**

   - **User Interface**: Built using React, the frontend is responsible for rendering the pages where users can:

     - View job recommendations

     - Filter jobs based on criteria like location, job type, salary, etc.

     - Apply for jobs or save them for later

     - Create and manage profiles

   - **Authentication**: React communicates with the backend to handle user login, registration, and profile management using tokens for authentication.

### 2. **Backend (Node.js + Express)**

   - **API Development**: Using Express, the backend provides APIs that allow the frontend to:

     - Fetch job listings

     - Handle applications

     - Manage user data and profiles

     - Perform search and filtering based on keywords, location, and other job-related parameters

   - **Authentication Middleware**: Middleware functions like `isAuthenticated` can be used to secure routes, ensuring only logged-in users can access certain endpoints.

### 3. **Database (MongoDB)**

   - **Job Listings**: Jobs are stored in MongoDB using a Mongoose model (e.g., `Job`). Each job entry may include fields like job title, description, company, salary, location, and required skills.

   - **User Profiles**: MongoDB stores user profiles, including resumes, experience, skills, and preferences, which help in customizing job recommendations.

   - **Application Tracking**: Tracks which users applied to which jobs and the status of those applications (e.g., pending, accepted, rejected).

### 4. **Recommendation Algorithm**

   - **User Preferences**: The system can recommend jobs based on user preferences, resume data, or search history.

   - **Matching Criteria**: Matching jobs with users may involve factors like:

     - Keywords matching job descriptions with user profiles

     - Location proximity

     - Experience level and skills alignment

### 5. **Role-based Access Control**

   - Different users (job seekers, employers, admins) have different levels of access. A flag-based approach (0, 1, 2) can be used to handle permissions.

     - **Job Seekers** (0): Can browse and apply for jobs.

     - **Employers** (1): Can post jobs, manage listings, and view applications.

     - **Admins** (2): Can manage both users and jobs, and moderate content.

### 6. **Key Features**

   - **Search and Filters**: Enables users to filter job listings by criteria like job type (full-time, part-time), location, industry, and salary.

   - **Resume Parsing**: Optionally, the system can parse uploaded resumes to auto-fill job applications and suggest relevant positions.

   - **Notifications**: Users can receive notifications about new job postings or application status updates.

This structure provides a flexible, scalable system for job recommendation, with a focus on user experience, security, and efficient data management.