Zero-Shot AutoML with Pretrained Models

Öztürk, Ekrem; Ferreira, Fábio Furlan; Jomaa, Hadi S.; Schmidt-Thieme, Lars; Grabocka, Josif; Hutter, Frank

doi:10.48550/arxiv.2206.08476

Cited by 1 publication

(1 citation statement)

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“…Pretrained models are the foundation for the Zero-Shot Recommendation AI Models explored in this study [33][34][35][36]. Pretrained models, having been trained on vast and diverse datasets, encapsulate a broad understanding of natural language, making them ideal for Zero-Shot Learning (ZSL) tasks.…”

Section: Pretrained Models For Zero-shot Learningmentioning

confidence: 99%

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: Pretrained Models For Zero-shot Learningmentioning

confidence: 99%