Time series analysis of a Web search engine transaction log

  • Authors:

  • Ying Zhang;Bernard J. Jansen;Amanda Spink

  • Affiliations:

  • The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, College of Engineering, The Pennsylvania State University, University Park, PA 16802, United States;329F Information Sciences and Technology Building, College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, United States;Faculty of Information Technology, Queensland University of Technology, Gardens Point Campus, GPO Box 2434, Brisbane, QLD 4001, Australia

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2009

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Abstract

In this paper, we use time series analysis to evaluate predictive scenarios using search engine transactional logs. Our goal is to develop models for the analysis of searchers' behaviors over time and investigate if time series analysis is a valid method for predicting relationships between searcher actions. Time series analysis is a method often used to understand the underlying characteristics of temporal data in order to make forecasts. In this study, we used a Web search engine transactional log and time series analysis to investigate users' actions. We conducted our analysis in two phases. In the initial phase, we employed a basic analysis and found that 10% of searchers clicked on sponsored links. However, from 22:00 to 24:00, searchers almost exclusively clicked on the organic links, with almost no clicks on sponsored links. In the second and more extensive phase, we used a one-step prediction time series analysis method along with a transfer function method. The period rarely affects navigational and transactional queries, while rates for transactional queries vary during different periods. Our results show that the average length of a searcher session is approximately 2.9 interactions and that this average is consistent across time periods. Most importantly, our findings shows that searchers who submit the shortest queries (i.e., in number of terms) click on highest ranked results. We discuss implications, including predictive value, and future research.