A Survey on Artificial Intelligence (AI) based Job Recommendation Systems

Patil, A. S.; Suwalka, Divyansh; Kumar, Aryan; Rai, Gaurav; Saha, Jayita

doi:10.1109/icscds56580.2023.10104718

Cited by 4 publications

(2 citation statements)

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“…The recruitment process, encompassing the analysis of job descriptions, candidate CV screening, shortlisting, interviews, and job offers, can be partially automated with the aid of Artificial Intelligence (AI) solutions. These systems, known as Job Recommendation Systems (JRS) [1][2][3], are designed to support these processes.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional JRS models, such as Content-Based, Collaborative Filtering, and Hybrid systems, while useful, often fail to capture the nuanced requirements of new job positions and the diverse skill sets of candidates. This shortfall is particularly evident in scenarios where there is a lack of sufficient historical data for novel roles, resulting in inadequate or biased recommendations [1]. Furthermore, existing JRS models tend to suffer from data biases, which can skew recommendations and perpetuate existing inequities in the recruitment process.…”

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confidence: 99%

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

Kurek,

Latkowski,

Bukowski

et al. 2024

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%