What type of study is this?

This is a Quantitative Study study.

September 20, 2025International Journal of Basic and Applied Sciences0 citationsOpen Access

Comparative Analysis of Job Recommendation Filtering Techniques

SSSachin SoniKadi Sarva Vishwavidyalaya SSS. I. ShahKadi Sarva Vishwavidyalaya

Key Points

The graph-enhanced hybrid model demonstrates superior accuracy compared to existing recommendation techniques.
Our evaluation assesses diversity, novelty, and fairness along with accuracy metrics, providing a comprehensive analysis.
Content-based filtering and collaborative filtering are key components compared against the proposed model in this study.
Improvements in skill-aware recommendations suggest significant implications for enhancing user experiences in job matching.

Abstract

The objective of the paper is to present a technical and novel comparative study of job recommendation filtering techniques, addressing gaps in existing research. We evaluate Content-Based (CBF), Collaborative Filtering (CF), and Hybrid models, introducing a Graph-Enhanced Hybrid Model that improves skill-aware recommendations using Graph Neural Networks (GNNs). Our evaluation includes accuracy, diversity, novelty, and fairness metrics, demonstrating superior performance over baselines.

AIに質問

Bookmark

View Full Paper