The Job Recommendation System is a collaborative filtering-based engine that suggests job postings to users based on their profiles. This system utilizes the Naukri.com dataset to provide personalized job recommendations to users, taking into account their preferences and similarities with other users.
The Job Recommendation System aims to assist job seekers in finding relevant job opportunities. It employs a collaborative filtering approach, where users with similar profiles are used to recommend job postings to each other. The system analyzes user profiles, job categories, locations, experience levels, and key skills to make accurate recommendations.
- Clone the repository
- Prepare the dataset:
- Obtain the Naukri.com dataset from Kaggle
- Ensure the dataset is in CSV format and contains relevant columns such as user profiles, job postings, and user interactions.
- Modify the code to match the dataset structure if necessary.
- Run the recommendation system:
- Open the Python script
job_recommendation_engine.py. - Adjust the configuration parameters and dataset file paths if needed.
- Execute the script:
python recommendation_system.py
- View the recommended job roles:
- The script will prompt you to enter a user ID or profile information.
- Based on the user input, the system will generate a list of recommended job roles.
- The job roles will be displayed in the console output.
The Job Recommendation System utilizes the Naukri.com dataset, which consists of job postings and user profiles. The dataset includes information such as job titles, salaries, experience requirements, skills, locations, and industry categories. The system preprocesses the data to create user profiles and job postings for recommendation generation.
The system employs collaborative filtering to recommend job postings to users. It calculates the similarity between user profiles using cosine similarity and identifies the nearest neighbors for each user. The system then suggests job postings based on the preferences and interactions of similar users. The recommendations are ranked by frequency or strength of interactions from the nearest neighbors.
To assess the performance of the recommendation system, various evaluation metrics can be used, such as precision, recall, or mean average precision. Additionally, splitting the dataset into training and testing sets allows for evaluating the accuracy and effectiveness of the system's recommendations.
Contributions are welcome! If you have suggestions or improvements for the Job Recommendation System, please open an issue or submit a pull request. Together, we can enhance the system and make it even more valuable for job seekers.