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An Adaptive Framework for Discovery and Mining of User Profiles from Social Web-Based Interest Communities

  • Nima DokoohakiEmail author
  • Mihhail Matskin
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN, volume 6)

Abstract

Within this paper we introduce an adaptive framework for semi- to fully-automatic discovery, acquisition and mining of topic style interest profiles from openly accessible social web communities. To do such, we build an adaptive taxonomy search tree from target domain (domain towards which we are gathering and processing profiles for), starting with generic concepts at root moving down to specific-level instances at leaves, then we utilize one of proposed Quest schemes to read the concept labels from the tree and crawl the source social network repositories for profiles containing matching and related topics. Using machine learning techniques, cached profiles are then mined in two consecutive steps, utilizing a clusterer and a classifier in order to assign and predict correct profiles to their corresponding clustered corpus, which are retrieved later on by an ontology-based recommender to suggest and recommend the community members with the items of their similar interest. Focusing on increasingly important digital cultural heritage context, using a set of profiles acquired from an openly accessible social network, we test the accuracy and adaptivity of framework. We will show that a tradeoff between schemes proposed can lead to adaptive discovery of highly relevant profiles.

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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.Software and Computer Systems (SCS), School of Information and Telecommunication Technology (ICT)Royal Institute of Technology (KTH)StockholmSweden
  2. 2.Computer and Information Science (IDI)Norwegian University of Science and Technology (NTNU)TrondheimNorway

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