Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process

Abstract

In the evolving realities of recruitment, the precision of job–candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as all-MiniLM-L6-v2 and applying similarity metrics like dot product and cosine similarity, we assessed their effectiveness in aligning job descriptions with candidate profiles. Our evaluations, based on Top-K Accuracy across various rankings, revealed a notable enhancement in matching accuracy compared to conventional methods. Specifically, the all-MiniLM-L6-v2 model with a chunk length of 768 exhibited outstanding performance, achieving a remarkable Top-1 accuracy of 3.35%, 55.45% for Top-100, and an impressive 81.11% for Top-500, establishing it as a highly effective tool for recruitment processes. This paper presents an in-depth analysis of these models, providing insights into their potential applications in real-world recruitment scenarios. Our findings highlight the capability of Zero-Shot Learning to address the dynamic requirements of the job market, offering a scalable, efficient, and adaptable solution for job–candidate matching and setting new benchmarks in recruitment efficiency.

Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph

“…With the rapid development of information technology and the internet, human society has quickly entered the information age. The application of technologies such as cloud computing, the Internet of Things, and big data on the internet has not only greatly enriched data resources but also led to a geometric increase in data scale [1,2]. This phenomenon has promoted the emergence of new disciplines and interdisciplinary integration, thereby driving an explosive growth in the number of academic papers [3].…”