Advertisement

Leveraging Site Search Logs to Identify Missing Content on Enterprise Webpages

  • Harsh Jhamtani
  • Rishiraj Saha Roy
  • Niyati Chhaya
  • Eric Nyberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10193)

Abstract

Online visitors often do not find the content they were expecting on specific pages of a large enterprise website, and subsequently search for it in site’s search box. In this paper, we propose methods to leverage website search logs to identify missing or expected content on webpages on the enterprise website, while showing how several scenarios make this a non-trivial problem. We further discuss how our methods can be easily extended to address concerns arising from the identified missing content.

References

  1. 1.
    Agresti, A., Kateri, M.: Categorical Data Analysis. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  2. 2.
    Biemann, C.: Chinese whispers: an efficient graph clustering algorithm and its application to natural language processing problems. In: TextGraphs-1 (2006)Google Scholar
  3. 3.
    Cui, M., Hu, S.: Search engine optimization research for website promotion. In: ICM 2011 (2011)Google Scholar
  4. 4.
    Freeman, E.A., Moisen, G.G.: A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol. Model. 217(1), 48–58 (2008)CrossRefGoogle Scholar
  5. 5.
    Lin, W., Liu, Y.: A novel website structure optimization model for more effective web navigation. In: WKDD 2008 (2008)Google Scholar
  6. 6.
    Yom-Tov, E., Fine, S., Carmel, D., Darlow, A.: Learning to estimate query difficulty including applications to missing content detection and distributed information retrieval. In: SIGIR 2005 (2005)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Harsh Jhamtani
    • 1
  • Rishiraj Saha Roy
    • 2
  • Niyati Chhaya
    • 3
  • Eric Nyberg
    • 1
  1. 1.Language Technology InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Max Planck Institute for Informatics, Saarland Informatics CampusSaarbrückenGermany
  3. 3.Big Data Experience LabAdobe ResearchBangaloreIndia

Personalised recommendations