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 have 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.
This workshop aims to bring together leading researchers and practitioners to exchange and share their experiences and latest research/application results on all aspects of Talent and Management Computing based on data mining technologies. It will provide a premier interdisciplinary forum to discuss the most 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 not limited to:
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, which will also be evaluated during the review process.
All the papers are required to be submitted via the EasyChair system.
14:00 - 14:05: Opening Remarks
14:05 - 14:35: Invited Talk: Prof. Xiong
14:35 - 15:00: Presentation 1: Discrepant Homophily Co-preserved Graph Convolutional Network for Labor Migration Forecasting
15:00 - 15:25: Presentation 2: Reconciling Methodological Paradigms within Talent Management Research: Exploring the Use of LLM as a Novice Qualitative Research Assistant
15:25 - 15:50: Presentation 3: Unsupervised Doctor Behavior Anomaly Detection with Self-Conditioned Diffusion Models
15:50 - 16:30: Coffee Break
16:30 - 16:55: Presentation 4: The Paradoxical Effect of Artificial Intelligence on Product Innovation
16:55 - 17:20: Presentation 5: Uncovering IT Career Path Patterns with Job Embedding-based Sequence Clustering
17:20 - 17:45: Presentation 6: Efficient Large-scale Online Recommender System
17:45 - 17:50: Closing Remarks
Host: Ying Sun, HKUST(GZ)
Hengshu Zhu
Career Science Lab (CSL), BOSS Zhipin
Yong Ge
The University of Arizona
Hui Xiong
The Hong Kong University of Science and Technology (Guangzhou)
Ee-Peng Lim
Singapore Management University