mlsanityData to 7 Oct 2026

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

arXiv 2307.03195 ↗First posted 3 Jul 2023cs.CY cs.AIcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 66 of its 401 references that are in the map. First posted on arXiv 3 Jul 2023.

Builds on

66 of 401 references.

References · 66

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 10; 2025: 1; 2026: 3.

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  1. 2025Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Educationcited by 13
  2. 2024Natural Language Processing for Human Resources: A Surveycited by 9
  3. 2023Enhancing Cognitive Diagnosis using Un-interacted Exercises: A Collaboration-aware Mixed Sampling Approachcited by 7
  4. 2024Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Modelscited by 7
  5. 2023ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awarenesscited by 6
  6. 2024RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processescited by 5
  7. 2026ScienceDB AI: An LLM-Driven Agentic Recommender System for Large-Scale Scientific Data Sharing Servicescited by 5
  8. 2024A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Predictioncited by 4
  9. 2024Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approachcited by 3
  10. 2024Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarkingcited by 3
  11. 2024DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendationcited by 2
  12. 2024Challenges and Opportunities of NLP for HR Applications: A Discussion Papercited by 1
5 more
  1. 2024Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learningcited by 1
  2. 2024CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationshipcited by 1
  3. 2026SciHorizon-GENE: Benchmarking LLM for Life Sciences Inference from Gene Knowledge to Functional Understandingcited by 1
  4. 2023The Initial Screening Order Problemcited by 0
  5. 2026Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentationcited by 0