• sharad soni
• Prof. Sumit Sharma

Job recommendation systems play a vital role in matching job seekers with relevant and suitable employment opportunities. In this study, we propose a novel system for job suggestion based on a Convolutional Neural Network (CNN) model. The system leverages the power of deep learning techniques to extract meaningful features from job descriptions and user profiles, enabling accurate and personalized job recommendations. The CNN model is trained on a large-scale dataset comprising job listings and user preferences, allowing it to learn complex patterns and relationships between job attributes and user preferences. Experimental results demonstrate that our system outperforms traditional recommendation approaches in terms of recommendation accuracy and relevance. Moreover, the CNN model provides interpretability by highlighting the key features and attributes that contribute to each recommendation. The proposed system shows great potential in improving the job search experience for users and assisting recruiters in finding suitable candidates. Future work includes further refinement of the CNN model, integration of additional data sources, and evaluation of the system's performance across different domains and user profiles.

Convolutional Neural Network, job recommendation, Random Forest, Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, AdaBoost, and Gradient Boosting.

" System for Job Suggestion Based on a Convolutional Neural Network (CNN) Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.e531-e537, July-2023, Available :http://www.jetir.org/papers/JETIR2307455.pdf

" System for Job Suggestion Based on a Convolutional Neural Network (CNN) Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppe531-e537, July-2023, Available at : http://www.jetir.org/papers/JETIR2307455.pdf