Information retrieval, fusion, completion, and clustering for employee expertise estimation
Abstract
Estimating the skills, talents, and expertise of employees is essential for human capital management in knowledge-based organizations across industries and sectors. In this paper, we describe an approach to infer the expertise of employees from their enterprise data and digital footprints. Using a novel big data workflow with components of information retrieval and search, data fusion, matrix completion, and ordinal regression clustering, we are able to automatically find evidence of expertise and determine appropriate evidence weights for different queries and data sources that we merge and present in a manner consumable by businesspeople. We illustrate the system on sample data from the IBM Corporation where it has been deployed.
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Forecasted performance is often used to estimate training needs.
“This forecasted performance is often used as the basis to set future goals or expectations (Angrave et al., 2016;Edwards & Edwards, 2019), to estimate the training needs (Horesh, Varshney, & Yi, 2016), to determine pay raise (Greenfield, 2018) or to promote employees to managerial positions (Mallafi & Widyantoro, 2016).”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Horesh, Varshney, & Yi, 2016
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Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732 · see citing works
Algorithms as work designers: How algorithmic management influences the design of jobs
Parent-Rocheleau, X., & Parker, S. K. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100838 · Cited by 512 · see citing works
- Authors
- Horesh, R., Varshney, K. R., & Yi, J.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- Venue
- 2016 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2016-12
- Volume
- 2016
- Pages
- 1385-1393
- OpenAlex
- W2583451245
- Cited by
- 14
- References
- 0
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 1 · 2019: 3 · 2018: 1 · 2017: 1
- FWCI
- 8.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- IEEE Conference Proceedings
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Keywords
- human capital management, information retrieval, data fusion, matrix completion, digital footprint, weight of evidence, big data workflows, knowledge-based organizations
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