Version 1
: Received: 16 July 2024 / Approved: 16 July 2024 / Online: 22 July 2024 (10:50:45 CEST)
How to cite:
Widodo, R. I. H.; Herdiyanto, R. F.; Thoriq, M.; Rahman, I. S. Job Recommendation System Combining Collaborative Filtering and Content Based Filtering. Preprints2024, 2024071700. https://doi.org/10.20944/preprints202407.1700.v1
Widodo, R. I. H.; Herdiyanto, R. F.; Thoriq, M.; Rahman, I. S. Job Recommendation System Combining Collaborative Filtering and Content Based Filtering. Preprints 2024, 2024071700. https://doi.org/10.20944/preprints202407.1700.v1
Widodo, R. I. H.; Herdiyanto, R. F.; Thoriq, M.; Rahman, I. S. Job Recommendation System Combining Collaborative Filtering and Content Based Filtering. Preprints2024, 2024071700. https://doi.org/10.20944/preprints202407.1700.v1
APA Style
Widodo, R. I. H., Herdiyanto, R. F., Thoriq, M., & Rahman, I. S. (2024). Job Recommendation System Combining Collaborative Filtering and Content Based Filtering. Preprints. https://doi.org/10.20944/preprints202407.1700.v1
Chicago/Turabian Style
Widodo, R. I. H., Muhammad Thoriq and Imam Sayyid Rahman. 2024 "Job Recommendation System Combining Collaborative Filtering and Content Based Filtering" Preprints. https://doi.org/10.20944/preprints202407.1700.v1
Abstract
The increasing complexity and volume of job listings in the digital era necessitate advanced recommendation systems to match job seekers with suitable opportunities. This paper presents a hybrid job recommendation system that integrates Collaborative Filtering (CF) and Content-Based Filtering (CBF) to enhance recommendation accuracy and user satisfaction. The CF component leverages historical user interactions and similarities between users to suggest jobs, while the CBF component analyzes job descriptions and user profiles to provide personalized recommendations based on individual preferences and qualifications. By combining these two approaches, the proposed system mitigates the limitations of each method and offers a more comprehensive and effective solution. Experimental results on a real-world dataset demonstrate the system's improved performance in terms of precision, recall, and F1-score compared to traditional recommendation models. This study contributes to the field of job recommendation systems by providing a robust framework that effectively bridges the gap between user needs and job market opportunities.
Keywords
machine learning; CBF; CF; job recommendation system
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
