Predicting and Recommending Skills in the Social Enterprise

Authors

  • Kush Varshney IBM Thomas J. Watson Research Center
  • Jun Wang IBM Thomas J. Watson Research Center
  • Aleksandra Mojsilović IBM Thomas J. Watson Research Center
  • Dongping Fang IBM Thomas J. Watson Research Center
  • John Bauer IBM Thomas J. Watson Research Center

DOI:

https://doi.org/10.1609/icwsm.v7i3.14474

Keywords:

recommender system, workforce analytics, expertise management, skills assessment

Abstract

In this paper, we discuss the need for accurate skills assessments of employees in large, global, client-facing enterprises and shortcomings of existing systems for obtaining and managing expertise. We describe enterprise and social data that can be mined to improve skill assessment processes. We propose a matrix completion approach with side information for improved skill assessment prediction and recommendation, and discuss how outputs can improve existing business process use cases and illuminate new ones.

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Published

2021-08-03

How to Cite

Varshney, K., Wang, J., Mojsilović, A., Fang, D., & Bauer, J. (2021). Predicting and Recommending Skills in the Social Enterprise. Proceedings of the International AAAI Conference on Web and Social Media, 7(3), 20-23. https://doi.org/10.1609/icwsm.v7i3.14474