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Article
CareerMapper: An automated resume evaluation tool
BigData 2016: Proceedings of the 4th IEEE International Conference on Big Data: Washington DC, December 5-8
  • Vivian LAI, Singapore Management University
  • Kyong Jin SHIM, Singapore Management University
  • Richard J. OENTARYO, Singapore Management University
  • Philips K. PRASETYO, Singapore Management University
  • Casey VU, Singapore Management University
  • Ee-peng LIM, Singapore Management University
  • David LO, Singapore Management University
Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2016
Abstract

The advent of the Web brought about major changes in the way people search for jobs and companies look for suitable candidates. As more employers and recruitment firms turn to the Web for job candidate search, an increasing number of people turn to the Web for uploading and creating their online resumes. Resumes are often the first source of information about candidates and also the first item of evaluation in candidate selection. Thus, it is imperative that resumes are complete, free of errors and well-organized. We present an automated resume evaluation tool called 'CareerMapper'. Our tool is designed to conduct a thorough review of a user's LinkedIn profile and provide best recommendations for improved online resumes by analyzing a large number of online user profiles.

Keywords
  • Job,
  • LinkedIn,
  • Recommendation,
  • Resume,
  • Candidate selection,
  • Evaluation tool
ISBN
9781467390057
Identifier
10.1109/BigData.2016.7841091
Publisher
IEEE
City or Country
Piscataway, NJ
Copyright Owner and License
Authors
Creative Commons License
Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International
Additional URL
https://doi.org/10.1109/BigData.2016.7841091
Citation Information
Vivian LAI, Kyong Jin SHIM, Richard J. OENTARYO, Philips K. PRASETYO, et al.. "CareerMapper: An automated resume evaluation tool" BigData 2016: Proceedings of the 4th IEEE International Conference on Big Data: Washington DC, December 5-8 (2016) p. 4005 - 4007
Available at: http://works.bepress.com/david_lo/210/