BRWM: A relevance feedback mechanism for web page clustering

被引:0
|
作者
Anagnostopoulos, Ioannis [1 ]
Anagnostopoulos, Christos [2 ]
Vergados, Dimitrios D.
Maglogiannis, Ilias
机构
[1] Univ Aegean, Dept Informat & Commun Syst Engn, Samos 83200, Greece
[2] Univ Aegean, Dept Cultural Technol & Commun, Lesvos 81100, Greece
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
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页码:44 / +
页数:3
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