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Scientific Programming
Volume 2015, Article ID 451476, 13 pages
http://dx.doi.org/10.1155/2015/451476
Research Article

Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks

1Universidad Carlos III de Madrid, Avenida Universidad 30, Leganés, 28911 Madrid, Spain
2Wrocław University of Technology, Wyspianskiego 27, 50-370 Wrocław, Poland
3Østfold University College, B R A Veien 4, 1783 Halden, Norway
4SRH University Berlin, Ernst-Reuter-Platz 10, 10587 Berlin, Germany

Received 3 February 2014; Revised 1 November 2014; Accepted 21 November 2014

Academic Editor: Przemyslaw Kazienko

Copyright © 2015 Jose María Álvarez-Rodríguez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The present paper introduces a hybrid technique to measure the expertise of users by analyzing their profiles and activities in social networks. Currently, both job seekers and talent hunters are looking for new and innovative techniques to filter jobs and candidates where candidates are trying to improve and make their profiles more attractive. In this sense, the Skillrank approach is based on the conjunction of existing and well-known information and expertise retrieval techniques that perfectly fit the existing web and social media environment to deliver an intelligent component to integrate the user context in the analysis of skills confidence. A major outcome of this approach is that it actually takes advantage of existing data and information available on the web to perform both a ranked list of experts in a field and a confidence value for every professional skill. Thus, expertise and experts can be detected, verified, and ranked using a suited trust metric. An experiment to validate the Skillrank technique based on precision and recall metrics is also presented using two different datasets: (1) ad hoc created using real data from a professional social network and (2) real data extracted from the LinkedIn API.