Computing social networks for information sharing: a case-based approach

  • Authors:
  • Rushed Kanawati;Maria Malek

  • Affiliations:
  • LIPN-CNRS UMR, Villetaneuse;LAPI-EISTI, Cergy

  • Venue:
  • OCSC'07 Proceedings of the 2nd international conference on Online communities and social computing
  • Year:
  • 2007

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Abstract

In this paper we describe a peer-to-peer approach that ails at allowing a group of like-minded people to share relevant documents in an implicit way. We suppose that user save their documents in a local user-defined hierarchy. the association between documents and hierarchy nodes (or folders) is used by a supervised hybrid neural-CBR classifier in order to learn the user classification strategy. This strategy is then used to compute correlations between local folders and remote ones allowing to recommend documents without having a shared hierarchy. Another CBR system is used to memorize how good queries are answered by peer agents allowing to learn a dynamic community of peer agents to be associated with each local folder.