Improving job matching with deep learning-based hyper-personalization

Qusai Q. Abuein, M. Shatnawi, Nour Alqudah
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Abstract

This study introduces a novel approach to streamline the recruitment process, benefiting both employers and job seekers. It leverages real-time personality-based classification to match candidates with the most suitable roles in a scalable and precise manner. This is achieved through machine learning-driven hyper-personalization, employing deep learning models to create a predictive language model. The study encompasses two key tasks: binary classification, distinguishing sentences containing soft skills (1) from those that do not (0), and multi-class classification, categorizing positive sentences into five classes based on Big Five personality traits. The research involved a series of experiments. Initially, multiple machine learning algorithms were employed to establish baseline models. Subsequently, the study investigated the impact of deep learning versus these baseline models. The results demonstrated an accuracy of 0.79% and 0.68% for binary classification tasks, and 0.79% and 0.60% for multi-class classification tasks, using Support Vector Machines in the machine learning task, and Bidirectional Long Short-Term Memory in the deep learning task, respectively. This approach showcases promise in revolutionizing the job matching process, offering a more efficient and accurate means of connecting individuals with their ideal employment opportunities based on their unique soft skills and personality traits.
利用基于深度学习的超个性化改进工作匹配
本研究介绍了一种简化招聘流程的新方法,使雇主和求职者都能从中受益。它利用基于个性的实时分类,以可扩展的精确方式将求职者与最合适的职位相匹配。这是通过机器学习驱动的超个性化来实现的,它采用深度学习模型来创建预测性语言模型。这项研究包括两项关键任务:二元分类,区分包含软技能(1)和不包含软技能(0)的句子;多类分类,根据大五人格特质将积极句子分为五类。研究涉及一系列实验。首先,采用多种机器学习算法建立基线模型。随后,研究调查了深度学习对这些基线模型的影响。结果表明,在机器学习任务中使用支持向量机,在深度学习任务中使用双向长短期记忆,二元分类任务的准确率分别为 0.79% 和 0.68%,多类分类任务的准确率分别为 0.79% 和 0.60%。这种方法有望彻底改变工作匹配过程,根据个人独特的软技能和个性特征,提供一种更高效、更准确的方法,将个人与理想的工作机会联系起来。
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