Interdisciplinary Matchmaking: Choosing Collaborators by Skill, Acquaintance and Trust

  • Albert Hupa
  • Krzysztof Rzadca
  • Adam Wierzbicki
  • Anwitaman Datta
Chapter

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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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Albert Hupa
    • 1
  • Krzysztof Rzadca
    • 2
  • Adam Wierzbicki
    • 3
  • Anwitaman Datta
    • 2
  1. 1.Institute of Applied Social SciencesUniversity of WarsawWarsawPoland
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Polish-Japanese Institute of Information TechnologyWarsawPoland

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