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.
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Kanawati, R., Malek, M. (2007). Computing Social Networks for Information Sharing: A Case-Based Approach. In: Schuler, D. (eds) Online Communities and Social Computing. OCSC 2007. Lecture Notes in Computer Science, vol 4564. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73257-0_10
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DOI: https://doi.org/10.1007/978-3-540-73257-0_10
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