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Related Experiment Videos

Traffic-based feedback on the web.

Jonathan Aizen1, Daniel Huttenlocher, Jon Kleinberg

  • 1Department of Computer Science, Cornell University, 4130 Upson Hall, Ithaca, NY 14850, USA.

Proceedings of the National Academy of Sciences of the United States of America
|January 8, 2004
PubMed
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Analyzing website usage data reveals item popularity shifts. These changes, often abrupt, correlate with external events and site highlights, offering insights into user interest dynamics.

Area of Science:

  • Web analytics
  • Information retrieval
  • Network science

Background:

  • Web usage data provides insights beyond content and link structure.
  • Understanding user interest dynamics is crucial for e-commerce and digital libraries.
  • Traditional analysis methods may miss real-time popularity shifts.

Purpose of the Study:

  • To develop a model for detecting significant changes in item popularity.
  • To analyze the relationship between item popularity changes and external events.
  • To leverage web usage data for understanding real-world event impacts.

Main Methods:

  • Defining and calculating 'batting average' as a measure of user interest (acquisition rate).
  • Developing a stochastic model to identify abrupt changes in batting average.

Related Experiment Videos

  • Experimenting with usage data from the Internet Archive.

    • Main Results:

    • Significant changes in item batting average often occur abruptly.
    • These popularity shifts correlate with on-site events (e.g., item highlighting) and external factors (e.g., external links).
    • Web usage data dynamics can reflect external events impacting item popularity.

    Conclusions:

    • Analyzing the dynamics of item popularity using batting average is effective.
    • Web traffic analysis can reveal the impact of both internal and external events.
    • This approach enhances understanding of user behavior and event influence on digital platforms.