NDLI: Quantifying and Recommending Expertise When New Skills Emerge

Content Provider	IEEE Xplore Digital Library
Author	Dongping Fang Varshney, K.R. Jun Wang Ramamurthy, K.N. Mojsilovic, A. Bauer, J.H.
Copyright Year	2013
Description	Author affiliation: Bus. Analytics & Math. Sci., IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA (Dongping Fang; Varshney, K.R.; Jun Wang; Ramamurthy, K.N.; Mojsilovic, A.; Bauer, J.H.)
Abstract	In the rapidly changing technological world of today, new technical areas emerge quickly, and new skills related to them garner high demand. In this paper, our goal is to recommend experts for new skills and skill topics. We propose multiple predictive models to utilize data from different enterprise sources: employee assessment data, free-text skill description data, and employee tags from corporate social media. These models include collaborative filtering, content-based, and novel hybrid recommendation approaches. We apply them in an empirical study of real-world corporate data, in which we compare and contrast the models to gain insight on the drivers of performance. The considered data is both structured and unstructured, messy, subjective, and incomplete. The central theme of the paper is to understand how to use data from different sources and what each data source contributes in the expertise management domain.
Sponsorship	Toshiba
Starting Page	672
Ending Page	679
File Size	266543
Page Count	8
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.33
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	cold-start problem enterprise social networks recommendation systems workforce analytics Taxonomy Collaboration Companies Predictive models Media Matrix decomposition expertise taxonomy
Content Type	Text
Resource Type	Article

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