Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks
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 i...
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Online Access: | http://dx.doi.org/10.1155/2015/451476 |
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doaj-56dc86f5d9054b8e949b296518c4e63d2021-07-02T02:55:33ZengHindawi LimitedScientific Programming1058-92441875-919X2015-01-01201510.1155/2015/451476451476Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social NetworksJose María Álvarez-Rodríguez0Ricardo Colomo-Palacios1Vladimir Stantchev2Universidad Carlos III de Madrid, Avenida Universidad 30, Leganés, 28911 Madrid, SpainØstfold University College, B R A Veien 4, 1783 Halden, NorwaySRH University Berlin, Ernst-Reuter-Platz 10, 10587 Berlin, GermanyThe 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.http://dx.doi.org/10.1155/2015/451476 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jose María Álvarez-Rodríguez Ricardo Colomo-Palacios Vladimir Stantchev |
spellingShingle |
Jose María Álvarez-Rodríguez Ricardo Colomo-Palacios Vladimir Stantchev Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks Scientific Programming |
author_facet |
Jose María Álvarez-Rodríguez Ricardo Colomo-Palacios Vladimir Stantchev |
author_sort |
Jose María Álvarez-Rodríguez |
title |
Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks |
title_short |
Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks |
title_full |
Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks |
title_fullStr |
Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks |
title_full_unstemmed |
Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks |
title_sort |
skillrank: towards a hybrid method to assess quality and confidence of professional skills in social networks |
publisher |
Hindawi Limited |
series |
Scientific Programming |
issn |
1058-9244 1875-919X |
publishDate |
2015-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2015/451476 |
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