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Area of Science:

  • Computer Science
  • Information Science

Background:

  • Maintaining optimal website hyperlink structures is challenging for human editors, especially on large, dynamic sites.
  • Identifying and implementing new, beneficial links can be resource-intensive due to manual page editing requirements.

Purpose of the Study:

  • To develop a data-driven method for automating the identification and placement of useful hyperlinks on websites.
  • To leverage implicit signals within server logs to predict the future usefulness of non-existent links.

Main Methods:

  • Developed an approach to automatically find useful hyperlinks by analyzing passively collected server logs.
  • Modeled the future usefulness of non-existent links based on implicit signals in server log data.
  • Defined and proposed an efficient algorithm for link placement under budget constraints.

Main Results:

  • Demonstrated the effectiveness of the approach on Wikipedia, using server logs and revision history for evaluation.
  • Showcased the method's applicability to other websites, including the biomedical research site Simtk.
  • Validated that standard server logs contain signals for predicting useful, yet-to-be-created links.

Conclusions:

  • Automated hyperlink placement using server log analysis is feasible and effective.
  • The proposed method offers a scalable solution for enhancing website navigation and content discoverability.
  • This data-driven approach reduces the manual effort required for website link optimization.