A Survey of Algorithms and Systems for Expert Location in Social Networks

Lappas, Theodoros; Liu, Kun; Terzi, Evimaria

doi:10.1007/978-1-4419-8462-3_8

Cited by 48 publications

(37 citation statements)

References 59 publications

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“…Esse tipo de problema é chamado de localização de especialistas sem restrições de grafos (LAPPAS, LIU;TERZI, 2011), ou relevância baseada em documentos (WANG et al, 2013). Entretanto, nesse cenário, não são consideradas as relações entre os vários especialistas, por exemplo, a relação de coautoria de um pesquisador com pesquisadores bem ranqueados faz deste pesquisador um potencial especialista em tal assunto.…”

Section: Novas Técnicas Os Expert Finding Systems (Efs) Também Conhunclassified

Algoritmo de classificação de especialistas em áreas na base de currículos Lattes

Chagas

Pérez-Alcázar

Digiampietri

2015

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Resumo: Este trabalho propõe um algoritmo para construir um ranking de especialistas na base de currículos Lattes. Para isto, foi elaborado um algoritmo composto por três estágios de processamento: Score Alfa, que analisa os títulos dos documentos baseado na ontologia definida para selecionar os assuntos e importâncias destes assuntos para cada publicação; Score Beta, que analisa a qualidade das publicações utilizando conceitos definidos pela CAPES para beneficiar produções publicadas em veículos mais importantes; e Score Propagated, que analisa a importância de estar bem conectado a demais pesquisadores especialistas propagando o conhecimento obtido. Para o teste de precisão do algoritmo, utilizaram-se dados reais da área de Nanotecnologia.Palavras-chave: Localização de especialistas. Currículos Lattes. Análise de Redes Sociais. Recuperação da informação.

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Section: Novas Técnicas Os Expert Finding Systems (Efs) Também Conhunclassified

Algoritmo de classificação de especialistas em áreas na base de currículos Lattes

Chagas

Pérez-Alcázar

Digiampietri

2015

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“…Identifying experts solely based on social networks has also been considered in previous research [46,47]. The idea behind these techniques is that an individual's expertise can be inferred from the expertise of other people to whom he or she is connected [27].…”

Section: Expert Identificationmentioning

confidence: 99%

Expert recommendation based on social drivers, social network analysis, and semantic data representation

Fazel-Zarandi

Devlin

Huang

et al. 2011

Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems

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Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous sources (such as collaboration networks of individuals, article citation networks, and concept networks) and depends significantly on the needs of individuals in seeking recommendations.Although over the past decade much effort has gone into developing techniques to increase and evaluate the quality of recommendations, personalizing recommendations according to individuals' motivations has not received much attention. While previous work in the literature has focused primarily on identifying experts, our focus here is on personalizing the selection of an expert through a principled application of social science theories to model the user's motivation. In this paper, we present an expert recommender system capable of applying multiple theoretical mechanisms to the problem of personalized recommendations through profiling users' motivations and their relations. To this end, we use the Multi-Theoretical Multi-Level (MTML) framework which investigates social drivers for network formation in the communities with diverse goals. This framework serves as the theoretical basis for mapping motivations to the appropriate domain data, heuristic, and objective functions for the personalized expert recommendation.As a proof of concept, we developed a prototype recommender grounded in social science theories, and utilizing computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating on data from heterogeneous sources. We evaluated the prototype's ability to predict collaborations for scientific research teams, using a simple off-line methodology. Preliminary results demonstrate encouraging success while offering significant personalization options and providing flexibility in customizing the recommendation heuristic based on users' motivations. In particular, recommendation heuristics based on different motivation profiles result in different recommendations, and taken as a whole better capture the diversity of observed expert collaboration.

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“…The above questions correspond to the expert location and expert team formation problem respectively, research topics that attract significant research lately [7]. For addressing the above research questions, we present an approach which is based on four different disciplines and attempts to combine them in a single model/system.…”

Section: Introductionmentioning

confidence: 99%

Software Project Team Selection Based on Enterprise Social Networks

Fitsilis

Gerogiannis

Ανθόπουλος

2015

Lecture Notes in Electrical Engineering

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Abstract. Project management is a well understood management method, widely adopted today, in order to give predictable results to complex problems. It is based on the assumption that unique undertakings require flexible organizational structures and different skill sets, in order to be implemented successfully. It is evident that matching the required, by each project, skills with the skills offered from the available human capital, is not a trivial process. This need of building high performing, effective teams with the appropriate skill set, is quite important since it is a prerequisite for successful project completion. Furthermore the recent shift to agile project management, together with the fact that projects today do not only require technical skills but also behavioral and contextual skills increases the importance of project team selection. In this paper, we present the ONSOCIAL system, a system that attempts to capitalize knowledge accumulated over social networks and to support the project team selection process. For achieving this objective, the ONSOCIAL system is using information stored in social network systems, analyses the stored profiles and offers recommendations for candidate team members.

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A Survey of Algorithms and Systems for Expert Location in Social Networks

Cited by 48 publications

References 59 publications

Algoritmo de classificação de especialistas em áreas na base de currículos Lattes

Algoritmo de classificação de especialistas em áreas na base de currículos Lattes

Expert recommendation based on social drivers, social network analysis, and semantic data representation

Software Project Team Selection Based on Enterprise Social Networks

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