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.