A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

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

Professional Network Matters: Connections Empower Person-Job Fit

Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions. While existing works leverage historical or contextual information, they often disregard a crucial aspect: job seekers' social relationships in professional networks. This paper emphasizes the importance of incorporating professional networks into the Person-Job Fit model. Our innovative approach consists of two stages: (1) defining a Workplace Heterogeneous Information Network (WHIN) to capture heterogeneous knowledge, including professional connections and pre-training representations of various entities using a heterogeneous graph neural network; (2) designing a Contextual Social Attention Graph Neural Network (CSAGNN) that supplements users' missing information with professional connections' contextual information. We introduce a job-specific attention mechanism in CSAGNN to handle noisy professional networks, leveraging pre-trained entity representations from WHIN. We demonstrate the effectiveness of our approach through experimental evaluations conducted across three real-world recruitment datasets from LinkedIn, showing superior performance compared to baseline models.Comment: Accepted at WSDM 202

Theory-driven Bilateral Dynamic Preference Learning for Person and Job Match: A Process-oriented Multi-step Multi-objective Method

Person-job matching is a typical dynamic process with bilateral interactions between job seekers and jobs, along with sample imbalance issues. These characteristics pose significant challenges when designing an intelligent person-job match method. In this paper, we propose a novel process-oriented view of the person-job matching problem and formulate it as a multi-step multi-objective bilateral match learning problem. Our method combines profile features and historical sequential behaviors to learn the bilateral attributes and dynamic preferences, with multimodal data integrated through various attention mechanisms, such as the orthogonal multi-head and gated mechanisms. The method includes a sequence update module to learn the bilateral preferences and their updates sensitive to feedback. Furthermore, the multi-step constraint effectively solves the problem of imbalanced samples through partial relationships and information transmission between multi-objectives. Abundant experiments show that our method outperforms state-of-the-art methods in providing successful matches and improving recruitment efficiency

TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit

Person-job fit is an essential part of online recruitment platforms in serving various downstream applications like Job Search and Candidate Recommendation. Recently, pretrained large language models have further enhanced the effectiveness by leveraging richer textual information in user profiles and job descriptions apart from user behavior features and job metadata. However, the general domain-oriented design struggles to capture the unique structural information within user profiles and job descriptions, leading to a loss of latent semantic correlations. We propose TAROT, a hierarchical multitask co-pretraining framework, to better utilize structural and semantic information for informative text embeddings. TAROT targets semi-structured text in profiles and jobs, and it is co-pretained with multi-grained pretraining tasks to constrain the acquired semantic information at each level. Experiments on a real-world LinkedIn dataset show significant performance improvements, proving its effectiveness in person-job fit tasks.Comment: ICASSP 2024 camera ready. 5 pages, 1 figure, 3 table

The Impact of AI on Recruitment and Selection Processes: Analysing the role of AI in automating and enhancing recruitment and selection procedures

Human resource management is the process of identifying, recruiting, hiring, and training talented individuals, as well as providing them with career advancement possibilities and critical feedback on their performance. The purpose of this study was to investigate the function of AI in HRM practises using qualitative bibliometric analysis. Scopus, emerald, and the Jstore library are used as data sources. This analysis contains adjustments to data spanning 18 years. It also showed that there is a constant improvement and introduction of new technological conveniences. In accordance with the present market climate, which promotes and celebrates process management and people management practises targeted at making the organisation economically viable and different from the competition, this is a positive development. This work advances the theoretical understanding of AI\u27s growth in the HR sector in light of this reality. Articles and proceedings examined in this research reveal that different authors and academic institutions provide different perspectives on the problem

AI based e-recruitment system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonModern web-based e-recruitment methods have revolutionised advertising, source tracking, and online inquiry forms with the associated start-up and maintenance costs. Attracting and hiring qualified candidates, navigating online recruiting tools, increasing unsuitable applications, and discrimination and diversity issues are just a few of the drawbacks of e recruitment. A platform with AI algorithms is developed to overcome limitations, especially for Saudi private and public sector recruiters who lack AI in their application processes. The Unified Theory of Acceptance and Use of Technology (UTAT) measured user acceptance of e-recruitment systems, with a Cronbach's alpha of 0.96 indicating high reliability. The platform and its features were evaluated using five-point Likert scales, with mean responses exceeding 3.4, indicating high acceptability. This PhD developed the Artificial Intelligent Recruitment (AIRec) platform, ranking candidates with 99 per cent accuracy. Improve corporate image and profile, reduce recruitment and overhead costs, use better tools to select candidates based on sound criteria, provide tracking for both candidates and employers. AIRec also aims to change HR and line management culture and behaviour. The platform and its contributions were tested in real-world scenarios in the top Saudi government and university recruiting bodies. Based on Cronbach's alpha testing and validation, the result was 0.97 out of 1. The results show the system's high reliability

AI Recruiting Tools at ShipIt2Me.com

In recent years, we have seen a dramatic increase in business interest in artificial intelligence (AI) and the number of companies that implement AI-related technologies. Thus, current and future employees need understand AI. In this paper, we present a teaching case based on a fictitious company for information systems or business courses at the undergraduate or graduate level. The case introduces students to ShipIt2Me.com (“ShipIt2Me”), a fictitious American e-commerce company that developed an AI human resources recruiting tool to help it hire cloud computing talent. The teaching case summarizes AI concepts and the opportunity for students to examine the advantages and disadvantages of using AI tools in human resources recruiting

AI-enabled exploration of Instagram profiles predicts soft skills and personality traits to empower hiring decisions

It does not matter whether it is a job interview with Tech Giants, Wall Street firms, or a small startup; all candidates want to demonstrate their best selves or even present themselves better than they really are. Meanwhile, recruiters want to know the candidates' authentic selves and detect soft skills that prove an expert candidate would be a great fit in any company. Recruiters worldwide usually struggle to find employees with the highest level of these skills. Digital footprints can assist recruiters in this process by providing candidates' unique set of online activities, while social media delivers one of the largest digital footprints to track people. In this study, for the first time, we show that a wide range of behavioral competencies consisting of 16 in-demand soft skills can be automatically predicted from Instagram profiles based on the following lists and other quantitative features using machine learning algorithms. We also provide predictions on Big Five personality traits. Models were built based on a sample of 400 Iranian volunteer users who answered an online questionnaire and provided their Instagram usernames which allowed us to crawl the public profiles. We applied several machine learning algorithms to the uniformed data. Deep learning models mostly outperformed by demonstrating 70% and 69% average Accuracy in two-level and three-level classifications respectively. Creating a large pool of people with the highest level of soft skills, and making more accurate evaluations of job candidates is possible with the application of AI on social media user-generated data