BRWM: A relevance feedback mechanism for web page clustering

  • Ioannis Anagnostopoulos
  • Christos Anagnostopoulos
  • Dimitrios D. Vergados
  • Ilias Maglogiannis
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 204)


This paper describes an information system, which classifies web pages in specific categories according to a proposed relevance feedback mechanism. The proposed relevance feedback mechanism is called Balanced Relevance Weighting Mechanism — BRWM and uses the proportion of the already relevant categorized information amount for feature classification. Experimental measurements over an e-commerce framework, which describes the fundamental phases of web commercial transactions verified the robustness of using the mechanism on real data. Except from revealing the accomplished sequences in a web commerce transaction, the system can be used as an assistant and consultation tool for classification purposes. In addition, BRWM was compared with a similar relevance feedback mechanism from the literature over the established corpus of Reuters-21578 text categorization test collection, presenting promising results.


Information Gain Relevance Feedback Electronic Market Breakeven Point Descriptor Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© International Federation for Information Processing 2006

Authors and Affiliations

  • Ioannis Anagnostopoulos
    • 1
  • Christos Anagnostopoulos
    • 2
  • Dimitrios D. Vergados
    • 1
  • Ilias Maglogiannis
    • 1
  1. 1.Department of Information and Communication Systems EngineeringUniversity of the AegeanKarlovassi, SamosGreece
  2. 2.Department of Cultural Technology and CommunicationMytiline LesvosGreece

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