This one is a good research paper
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
references:
https://faculty.ist.psu.edu/jjansen/academic/jansen_time_series_analysis.pdf
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