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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 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 precisionThe 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.show moreshow less

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Metadaten
Author: Jose María Álvarez-Rodríguez, Ricardo Colomo-Palacios, Prof. Dr. Vladimir Stantchev
URN:urn:nbn:de:0298-opus4-7264
Parent Title (English):Scientific Programming
Document Type:Article
Language:English
Date of Publication (online):2015/08/18
Year of first Publication:2015
Publishing Institution:SRH Berlin University of Applied Sciences
Release Date:2015/09/03
Page Number:14
Link author profile:https://www.srh-berlin.de/hochschule/hochschulteam/stantchev-vladimir/
Impact Factor:0.559
Institutes:Institute of Information Systems (IIS, SRH Berlin)
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung
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