About this Research Topic
This Research Topic, developed in conjunction with the 4th Workshop on Recommender Systems for Human Resources (RecSys in HR 2024), explores the dynamic interplay between Artificial Intelligence (AI) and Human Resources (HR) Technologies. Focusing on Recommender Systems (RecSys) as a prime example of AI applications, this themed article collection provides a comprehensive view of their role in the HR domain.
Amid the widespread adoption of AI technologies in HR, with over 250 commercial AI-based tools available, this collection serves as a platform to examine the specific aspects, challenges, and opportunities of integrating RecSys and other HR Tech applications.
Encompassing the entire HR spectrum, beyond recruitment, the edition emphasizes RecSys in the full employee lifecycle, up- and re-skilling, retention, compensation, and broader economic and societal consequences. It delves into under-represented research areas like explainability, fairness, and ethics in RecSys within HR.
Recent global developments, such as labour shortages, economic shifts, and impending legal changes, underscore the urgency and relevance of this edition. The rise of Large Language Models and generative AI, along with imminent legislation like the EU AI Act, further highlights the evolving landscape of HR Tech.
Contributions are welcomed from academia, industry, and government, fostering a multidisciplinary approach within the Recommender Systems section. Submissions may include original research on RecSys and HR Tech applications, interfaces for decision-making tools, considerations of bias and ethics, and discussions on economic and societal consequences. The editors encourage submissions on novel recommendation approaches, user studies, case studies, and expert recommendations, all framed within Recommender Systems as a key AI application in HR.
This Research Topic aims to significantly contribute to understanding the intricate relationship between AI technologies, particularly Recommender Systems, and the evolving landscape of Human Resources within the realm of Big Data.
Amid the widespread adoption of AI technologies in HR, with over 250 commercial AI-based tools available, this collection serves as a platform to examine the specific aspects, challenges, and opportunities of integrating RecSys and other HR Tech applications.
Encompassing the entire HR spectrum, beyond recruitment, the edition emphasizes RecSys in the full employee lifecycle, up- and re-skilling, retention, compensation, and broader economic and societal consequences. It delves into under-represented research areas like explainability, fairness, and ethics in RecSys within HR.
Recent global developments, such as labour shortages, economic shifts, and impending legal changes, underscore the urgency and relevance of this edition. The rise of Large Language Models and generative AI, along with imminent legislation like the EU AI Act, further highlights the evolving landscape of HR Tech.
Contributions are welcomed from academia, industry, and government, fostering a multidisciplinary approach within the Recommender Systems section. Submissions may include original research on RecSys and HR Tech applications, interfaces for decision-making tools, considerations of bias and ethics, and discussions on economic and societal consequences. The editors encourage submissions on novel recommendation approaches, user studies, case studies, and expert recommendations, all framed within Recommender Systems as a key AI application in HR.
This Research Topic aims to significantly contribute to understanding the intricate relationship between AI technologies, particularly Recommender Systems, and the evolving landscape of Human Resources within the realm of Big Data.
Keywords: Recommender Systems, Human Resources
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
