485 research outputs found

    A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

    Full text link
    In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure

    Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks

    Full text link
    Numerous HR applications are centered around resumes and job descriptions. While they can benefit from advancements in NLP, particularly large language models, their real-world adoption faces challenges due to absence of comprehensive benchmarks for various HR tasks, and lack of smaller models with competitive capabilities. In this paper, we aim to bridge this gap by introducing the Resume-Job Description Benchmark (RJDB). We meticulously craft this benchmark to cater to a wide array of HR tasks, including matching and explaining resumes to job descriptions, extracting skills and experiences from resumes, and editing resumes. To create this benchmark, we propose to distill domain-specific knowledge from a large language model (LLM). We rely on a curated skill-occupation graph to ensure diversity and provide context for LLMs generation. Our benchmark includes over 50 thousand triples of job descriptions, matched resumes and unmatched resumes. Using RJDB, we train multiple smaller student models. Our experiments reveal that the student models achieve near/better performance than the teacher model (GPT-4), affirming the effectiveness of the benchmark. Additionally, we explore the utility of RJDB on out-of-distribution data for skill extraction and resume-job description matching, in zero-shot and weak supervision manner. We release our datasets and code to foster further research and industry applications

    Automation of companies’ recruitment process: development of an algorithm capable of ranking CVs according to job offers

    Get PDF
    Dissertação de mestrado integrado em Informatics EngineeringThis document presents a Thesis and describes the underlying work which was developed along the second year of the Master Degree in Informatics Engineering offered by Departamento de Informática of Universidade do Minho and accomplished at Syone SBS Software – Tecnologia e Serviços de Informática, S.A.. In the past few years, some attempts to automatically screening CVs with resource to Natural Language Processing have been made not only to save recruiters’ time, but also to spare them the most tedious task of the recruitment process and, consequently, smooth their job. However, the majority is still very primitive, misclassifies a lot of CVs and needs a deeper study. Therefore, the aim of this Master’s Project is precisely to develop an algorithm that is capable of automatically ranking candidates’ CVs according to their similarity regarding the job offer they applied for. Thus, a general architecture was proposed where CVs and job offers are preprocessed, in order to obtain the respective texts proper to be further processed. That said, two different approaches were followed, in order to find the similarity between the documents in question. To do so, the first approach resorted to several Machine Learning algorithms and similarity measures, while the second approach structured the initial documents to compare their respective information. After that, tests were conducted to evaluate both approaches and enable the comparison between them. Finally, the conclusions were drawn and also reported in this dissertation.Este documento apresenta uma Tese e descreve o trabalho subjacente que foi desenvolvido ao longo do segundo ano do Mestrado em Engenharia Informática do Departamento de Informática da Universidade do Minho e realizado na Syone SBS Software – Tecnologia e Serviços de Informática, S.A.. Nos últimos anos, algumas tentativas de triagem automática de currículos com recurso a Processamento de Linguagem Natural foram feitas não só para economizar o tempo dos recrutadores, mas também para os poupar da tarefa mais entediante do processo de recrutamento e, consequentemente, suavizar o seu trabalho. Contudo, a maioria ainda é muito primitiva, classifica incorretamente muitos currículos e necessita de um estudo mais aprofundado. Sendo assim, o objetivo deste Projeto de Mestrado é precisamente desenvolver um algoritmo capaz de classificar automaticamente os currículos dos candidatos de acordo com a sua similaridade relativamente à oferta de emprego a que se candidataram. Deste modo, foi proposta uma arquitetura geral onde os CVs e as ofertas de emprego são pré-processados, de forma a obter os respetivos textos adequados para posterior processamento. Dito isto, foram seguidas duas abordagens distintas, de forma a encontrar a semelhança entre os documentos em questão. Para tal, a primeira abordagem recorreu a diversos algoritmos de Aprendizagem Automática e medidas de similaridade, enquanto a segunda abordagem estruturou os documentos iniciais para comparar as suas respetivas informações. De seguida, foram realizados testes para avaliar ambas as abordagens e possibilitar a comparação entre elas. Por fim, as conclusões foram tiradas e também relatadas nesta dissertação

    Determining systematic differences in human graders for machine learning-based automated hiring

    Get PDF
    Firms routinely utilize natural language processing combined with other machine learning (ML) tools to assess prospective employees through automated resume classification based on pre-codified skill databases. The rush to automation can however backfire by encoding unintentional bias against groups of candidates. We run two experiments with human evaluators from two different countries to determine how cultural differences may affect hiring decisions. We use hiring materials provided by an international skill testing firm which runs hiring assessments for Fortune 500 companies. The company conducts a video-based interview assessment using machine learning, which grades job applicants automatically based on verbal and visual cues. Our study has three objectives: to compare the automatic assessments of the video interviews to assessments of the same interviews by human graders in order to assess how they differ; to examine which characteristics of human graders may lead to systematic differences in their assessments; and to propose a method to correct human evaluations using automation. We find that systematic differences can exist across human graders and that some of these differences can be accounted for by an ML tool if measured at the time of training

    Improving Asynchronous Interview Interaction with Follow-up Question Generation

    Get PDF
    The user experience of an asynchronous video interview system, conventionally is not reciprocal or conversational. Interview applicants expect that, like a typical face-to-face interview, they are innate and coherent. We posit that the planned adoption of limited probing through follow-up questions is an important step towards improving the interaction. We propose a follow-up question generation model (followQG) capable of generating relevant and diverse follow-up questions based on the previously asked questions, and their answers. We implement a 3D virtual interviewing system, Maya, with capability of follow-up question generation. Existing asynchronous interviewing systems are not dynamic with scripted and repetitive questions. In comparison, Maya responds with relevant follow-up questions, a largely unexplored feature of irtual interview systems. We take advantage of the implicit knowledge from deep pre-trained language models to generate rich and varied natural language follow-up questions. Empirical results suggest that followQG generates questions that humans rate as high quality, achieving 77% relevance. A comparison with strong baselines of neural network and rule-based systems show that it produces better quality questions. The corpus used for fine-tuning is made publicly available

    How chatbots are used in recruitment and selection practices?

    Get PDF
    In the modern era, Artificial Intelligence (AI) has affected different functions of businesses, including Human Resources, in recruitment processes. With Chatbots (conversational agents) systems in place, HR can perform tasks like identifying, selecting, and interviewing talented candidates with more speed and consequently focus on strategic goals more effectively. This study aims to assess the current state of chatbot usage in HR processes in organisations, particularly in Higher Education Institutions (HEIs). For Part Ⅰ, the chatbot’s role is evaluated in detail for each stage of recruitment (i.e., sourcing, screening, selection and onboarding of candidates). Moreover, we will discuss how chatbot providers develop this service in terms of needed technical technologies and communicational aspects. The findings will help identify the best practices in developing better chatbots that align with the demands of modern hiring. In addition, we investigate chatbots’ impact on higher education with the rise of online learning and the Covid-19 pandemic. In part two, we develop a chatbot using the Google DialogFlow platform to support the admission process for prospective PhD students in the Doctoral Management Program of the UPC. This FAQ bot will be implemented as a supplementary channel in the doctoral program website to understand students’ queries and provide predefined answers. A survey is conducted based on the TAM framework to assess the chatbot’s functionality, quality, and intention of use. Based on the responses and findings, we will discuss how chatbots are a viable option to create new innovative services that are helpful for both candidates and educators. In the end, based on lessons learned, we propose conclusions, discussion and several recommendations for these intelligent systems. It is hoped that this work will open up new research possibilities for future optimisations in the fields of chatbots and recruitment in the future.En la era moderna, la Inteligencia Artificial (IA) ha afectado a diferentes funciones de las empresas, incluida la de Recursos Humanos, en los procesos de contratación. Con los sistemas de Chatbots (agentes conversacionales) implementados, HR puede realiza r tareas como identificar, seleccionar y entrevistar a personas candidat as talentos a s con más velocidad y, en consecuencia, enfocarse en objetivos estratégicos de manera más efectiva. Este estudio tiene como objetivo evaluar el estado actual del uso de chatbots en los procesos de recursos humanos en las organizaciones, particularmente en las Instituciones de Educación Superior (IES). Para la Parte Ⅰ, el rol del chatbot se evalúa en detalle para cada etapa del reclutamiento (i.e., planificación, abastecimiento, selec ción, verificación de referencias, selección e incorporación de candidatos). Además, discutiremos cómo los proveedores de chatbots desarrollan este servicio en términos de tecnologías técnicas necesarias y aspectos de comunicación. Los hallazgos ayudarán a identificar las mejores prácticas para desarrollar mejores chatbots que se alineen con las demandas de la contratación moderna. Además, investigamos el impacto de los chatbots en la educación superior con el aumento del aprendizaje en línea y la pandemia de Covid19. En la segunda parte, desarrollamos un chatbot utilizando la plataforma Google DialogFlow para apoyar el proceso de admisión de futuros estudiantes de doctorado Doctorado de la UPC. Este bot de preguntas en el Programa de Gestión de frecuentes se implementará como un canal complementario en el sitio web del programa de doctorado para comprender las consultas de los estudiantes y proporcionar respuestas predefinidas. Se realiza una encuesta basada en el marco TAM para evaluar la funcio nalidad, la calidad y la intención de uso del chatbot. Según las respuestas y los hallazgos, analizaremos cómo los chatbots son una opción viable para crear nuevos servicios innovadores que sean útiles tanto para los personas como para los educadores. Al candidat as final, en base a las lecciones aprendidas, proponemos conclusiones, discusión y varias recomendaciones para estos sistemas inteligentes. Se espera que este trabajo abra nuevas posibilidades de investigación para futuras optimizaciones en los campos de los chatbots y el reclutamiento en el futur
    • …
    corecore