Identifying User Behavior in domain-specific Repositories

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📝 Original Info

  • Title: Identifying User Behavior in domain-specific Repositories
  • ArXiv ID: 1403.7899
  • Date: 2014-04-01
  • Authors: Researchers from original ArXiv paper

📝 Abstract

This paper presents an analysis of the user behavior of two different domain-specific repositories. The web analytic tool etracker was used to gain a first overall insight into the user behavior of these repositories. Moreover, we extended our work to describe an apache web log analysis approach which focuses on the identification of the user behavior. Therefore the user traffic within our systems is visualized using chord diagrams. We could find that recommendations are used frequently and users do rarely combine searching with faceting or filtering.

💡 Deep Analysis

Deep Dive into Identifying User Behavior in domain-specific Repositories.

This paper presents an analysis of the user behavior of two different domain-specific repositories. The web analytic tool etracker was used to gain a first overall insight into the user behavior of these repositories. Moreover, we extended our work to describe an apache web log analysis approach which focuses on the identification of the user behavior. Therefore the user traffic within our systems is visualized using chord diagrams. We could find that recommendations are used frequently and users do rarely combine searching with faceting or filtering.

📄 Full Content

This paper presents an analysis of the user behavior of two different domain-specific repositories. The web analytic tool etracker was used to gain a first overall insight into the user behavior of these repositories. Moreover, we extended our work to describe an apache web log analysis approach which focuses on the identification of the user behavior. Therefore the user traffic within our systems is visualized using chord diagrams. We could find that recommendations are used frequently and users do rarely combine searching with faceting or filtering.

Reference

This content is AI-processed based on ArXiv data.

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