TY - GEN
T1 - The 6th International Workshop on Talent and Management Computing (TMC 2025)
AU - Zhu, Hengshu
AU - Ge, Yong
AU - Xiong, Hui
AU - Lim, Ee Peng
N1 - Publisher Copyright:
© 2025 Owner/Author.
PY - 2025/8/3
Y1 - 2025/8/3
N2 - In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with 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 leaders to deliver intelligence for effective management for organizations. In the past few years, talent and management computing have increasingly attracted attention from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 6th International Workshop on Talent and Management Computing (TMC 2025), is to bring together researchers and practitioners to discuss critical problems faced by talent and management-related domains and potential data-driven solutions.
AB - In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with 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 leaders to deliver intelligence for effective management for organizations. In the past few years, talent and management computing have increasingly attracted attention from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 6th International Workshop on Talent and Management Computing (TMC 2025), is to bring together researchers and practitioners to discuss critical problems faced by talent and management-related domains and potential data-driven solutions.
KW - professional social networks
KW - talent behavior modeling
UR - https://www.scopus.com/pages/publications/105014375427
UR - https://www.scopus.com/pages/publications/105014375427#tab=citedBy
U2 - 10.1145/3711896.3737851
DO - 10.1145/3711896.3737851
M3 - Conference contribution
AN - SCOPUS:105014375427
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 6316
EP - 6317
BT - KDD 2025 - Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025
Y2 - 3 August 2025 through 7 August 2025
ER -