TMC 2026

INTRODUCTION

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with the talent and management related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision making and management for their organizations. In the past few years, Talent and Management Computing has increasingly attracted attentions from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop is to bring together researchers and practitioners to discuss both the critical problems faced by talent and management related domains, and potential data-driven solutions by leveraging state-of-the-art data mining technologies.

In particular, given the remarkable technological advancements enabled by Large Language Models (LLMs), in this year, we intend to launch a demo track dedicated to exploring the use of LLMs in talent and management computing. This initiative is designed to highlight innovative applications leveraging LLMs to tackle essential challenges in talent acquisition, development, retention, and workforce optimization, among others. We have also initiated the IEEE P3154.1 recommended practice to establish a standardized framework for the application of AI agents in talent service.

TOPICS OF INTEREST

This workshop aims to bring together leading researchers and practitioners to exchange and share their experiences and latest research/application results on Talent and Management Computing based on data mining technologies. It will provide a premier interdisciplinary forum to discuss recent trends, innovations, applications as well as the real-world challenges encountered and corresponding data-driven solutions in relevant domains.

The topics of interest include but are not limited to:

Online recruitment
Job recommendation
Person-job fit and job satisfaction
Career development
Career path modeling
Professional social networks
Talent behavior modeling
Talent personality and leadership
Talent performance assessment
Talent retention and incentive
Team formation and task assignment
Group-based decision-making
Organizational change and stability
Organizational culture and communication
Organizational competition analysis
Labour market intelligence
Strategic management and planning
Fairness in talent and management computing
LLM-based talent management system

CALL FOR PAPER

We invite the submission of regular research papers (8 pages), as well as vision papers and short technical papers (around 4-6 pages), including all content and references. Submissions must be in PDF format, and formatted according to the new Standard ACM Conference Proceedings Template.

To encourage the discussion, both original papers, and papers which have been published before, are all welcome to be submitted to this workshop. Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Considering the practical characters of this workshop, to enrich the presentations, we strongly encourage the authors to submit their demonstrations, e.g., intelligent system for talent analytics, LLM-based talent management systems.

This year, we also launch a demo track dedicated to exploring the use of LLMs in talent and management computing, as well as the consideration about the application risk of LLMs in talent and management computing.

All the papers are required to be submitted via the EasyChair system. (Submission link coming soon)