.png){kind=icon}
.png){kind=icon}
Return
Latent Ability Model: A Generative Probabilistic Learning Framework for Workforce Analytics
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2018.2848658.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As more business workflow systems are being deployed in modern enterprises and organizations, more employee-activity log data are being collected and analyzed. In this paper, we develop a latent ability model (LAM) as a generative probabilistic learning framework for workforce analytics over employee-activity logs. The LAM development is novel in three aspects. First, we introduce the concept of latent ability variables to model hidden relations between employees and activities in terms of job performance, such as the set of skills provided by an employee and the set of skills required by an activity, and how well they matchup in employee-activity assignment. Second, we construct the latent ability model by learning latent ability parameters from the employee-activity log data using expectation-maximization and gradient descent. Finally, we leverage LAM to build inference and prediction models for employee performance prediction, employee ability comparison, and employee-activity matchup quality estimation. We evaluate the accuracy and efficiency of our approach using real log datasets collected from a workflow system deployed in the government of the city of Hangzhou, China, which consists of 5,287,621 log records over two years involving 744 activities and 1,725 employees. We show that LAM approach outperforms existing representative methods in both accuracy and efficiency.
Keywords:
Workforce analytics
generative model
graphical model
latent variable model
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.4
Papers:
6.8K
Citations:
3.2W