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Category-Based Audience Metrics for Web Site Content Improvement Using Ontologies and Page Classification

  • Jean-Pierre Norguet
  • Benjamin Tshibasu-Kabeya
  • Gianluca Bontempi
  • Esteban Zimányi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3999)

Abstract

With the emergence of the World Wide Web, analyzing and improving Web communication has become essential to adapt the Web content to the visitors’ expectations. Web communication analysis is traditionally performed by Web analytics software, which produce long lists of page-based audience metrics. These results suffer from page synonymy, page polysemy, page temporality, and page volatility. In addition, the metrics contain little semantics and are too detailed to be exploited by organization managers and chief editors, who need summarized and conceptual information to take high-level decisions. To obtain such metrics, we propose to classify the Web site pages into categories representing the Web site topics and to aggregate the page hits accordingly. In this paper, we show how to compute and visualize these metrics using OLAP tools. To solve the page-temporality issue, we propose to classify the versions of the pages using automatic classifiers.

Keywords

Organization Manager Word Sense Disambiguation Hierarchical Aggregation Content Journal Advance Information System Engineer 
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

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jean-Pierre Norguet
    • 1
  • Benjamin Tshibasu-Kabeya
    • 2
  • Gianluca Bontempi
    • 2
  • Esteban Zimányi
    • 1
  1. 1.Department of Computer & Network EngineeringUniversité Libre de BruxellesBrusselsBelgium
  2. 2.Machine Learning Group, Département d’InformatiqueUniversité Libre de BruxellesBrusselsBelgium

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