Talk · June 1, 2023

Design and Evaluation of a Personalized Job Recommendation System for Computer Science Students Using Hybrid Approach

Amina Shaikym, Zhadyra Zhalgassova, Ualikhan Sadyk

Abstract

This paper presents the design and evaluation of a job recommendation system for computer science students. The system utilizes advanced algorithms and feature selection techniques to provide personalized job recommendations for students based on their skills, interests, and career goals. The performance of the system was evaluated using a dataset of job postings and student profiles, and compared with existing job recommendation systems. Our evaluation results showed that our system outperformed existing systems in terms of all evaluation metrics, including accuracy, precision, recall, and F1-score. Our findings demonstrate the potential of personalized job recommen-dation systems for students and highlight the importance of advanced algorithms and feature selection techniques in improving the accuracy and personalization of job recommendations. The implications of our study for the field of job recommendation systems for students are discussed, including the potential for practical applications and future research directions. Overall, our study contributes to the advancement of knowledge in the field of job recommendation systems and provides a foundation for future research and system design in this area.

Recommender Systems and TechniquesOnline Learning and AnalyticsIntelligent Tutoring Systems and Adaptive LearningPersonalizationComputer scienceRecommender system

Design and Evaluation of a Personalized Job Recommendation System for Computer Science Students Using Hybrid Approach

Abstract

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