Measuring expertise similarity in expert networks

Boeva, Veselka; Krusheva, Maria; Tsiporkova, Elena

doi:10.1109/is.2012.6335190

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…Namely, the process of merging expert profiles is driven by the calculation of the similarity scores between different entities composing the profile, e.g., expert name, affiliation, email address, etc. (Boeva et al, 2012).…”

Section: Profiling Of Expertisementioning

confidence: 99%

An expertise Recommender System Based on Data from an Institutional Repository (DiVA)

Angelova

Devagiri

Boeva

et al. 2018

22nd International Conference on Electronic Publishing

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Section: Profiling Of Expertisementioning

confidence: 99%

An expertise Recommender System Based on Data from an Institutional Repository (DiVA)

Angelova

Devagiri

Boeva

et al. 2018

22nd International Conference on Electronic Publishing

Self Cite

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“…In the field of information retrieval, expertise retrieval [36], and expert finding [37] systems have been widely studied to provide a new approach to tackle the discovery of experts in some area [15]. Currently, practices as such competitions [38] or challenges are common techniques to retrieve experts based on their performance in a certain topic.…”

Section: State Of the Artmentioning

confidence: 99%

“…LinkedIn users selfreport their expertise and ask members of their social network to provide positive references or recommendations for them [13]. Although LinkedIn has been used in the literature for expertise search [14,15], to the best of authors knowledge, there is not a study devoted to the application of self-disclosure and social network integrators to assess the quality and confidence of professional skills in this network.…”

Section: Introductionmentioning

confidence: 99%

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

Álvarez-Rodríguez

Colomo‐Palacios

Stantchev

2015

Scientific Programming

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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.

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