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Interdisciplinary Matchmaking: Choosing Collaborators by Skill, Acquaintance and Trust

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Computational Social Network Analysis

Part of the book series: Computer Communications and Networks ((CCN))

Abstract

Social networks are commonly used to enhance recommender systems. Most of such systems recommend a single resource or a person. However, complex problems or projects usually require a team of experts that must work together on a solution. Team recommendation is much more challenging, mostly because of the complex interpersonal relations between members. This chapter presents fundamental concepts on how to score a team based on members’ social context and their suitability for a particular project. We represent the social context of an individual as a three-dimensional social network (3DSN) composed of a knowledge dimension expressing skills, a trust dimension and an acquaintance dimension. Dimensions of a 3DSN are used to mathematically formalize the criteria for prediction of the team’s performance. We use these criteria to formulate the team recommendation problem as a multi-criteria optimization problem. We demonstrate our approach on empirical data crawled from two web2.0 sites: onephoto.net and a social networking site. We construct 3DSNs and analyze properties of team’s performance criteria.

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Acknowledgements

This project has been supported by research grants no: 69/N-SINGAPUR/ 2007/0 and no: N N516 4307 33 of the Polish Ministry of Science and Higher Education and by Singapore A-STAR grant no: 072 134 0055.

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Authors and Affiliations

  1. Institute of Applied Social Sciences, University of Warsaw, Warsaw, Poland

    Albert Hupa

  2. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Krzysztof Rzadca & Anwitaman Datta

  3. Polish-Japanese Institute of Information Technology, Warsaw, Poland

    Adam Wierzbicki

Authors
  1. Albert Hupa
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  2. Krzysztof Rzadca
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  3. Adam Wierzbicki
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  4. Anwitaman Datta
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Corresponding author

Correspondence to Albert Hupa .

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Editors and Affiliations

  1. Scientific Network for Innovation &, Machine Intelligence Research Labs (MIR), Auburn, 98071-2259, U.S.A.

    Ajith Abraham

  2. Fac. Computers & Information, Cairo University, Ahmed Zewal Street 5, Giza, 12613, Egypt

    Aboul-Ella Hassanien

  3. Dept. Computer Science, Technical University Ostrava, Tr. 17. Listopadu 15, Ostrava, 708 33, Czech Republic

    Vaclav Sná¿el

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Hupa, A., Rzadca, K., Wierzbicki, A., Datta, A. (2010). Interdisciplinary Matchmaking: Choosing Collaborators by Skill, Acquaintance and Trust. In: Abraham, A., Hassanien, AE., Sná¿el, V. (eds) Computational Social Network Analysis. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-84882-229-0_12

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  • DOI: https://doi.org/10.1007/978-1-84882-229-0_12

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-228-3

  • Online ISBN: 978-1-84882-229-0

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