Why We Search: Visualizing and Predicting User Behavior
Eytan Adar (University of Washington, CSE)
Daniel Weld (University of WashingtonCSE)
Brian Bershad (University of WashingtonCSE)
Steven Gribble (University of Washington)
The aggregation and comparison of behavioral patterns on the WWW represent a tremendous opportunity for understanding past behaviors and predicting future behaviors. In this paper, we take a first step at achieving this goal. We present a large scale study correlating the behaviors of Internet users on multiple systems ranging in size from 27 million queries to 14 million blog posts to 20,000 news articles. We formalize a model for events in these time-varying datasets and study their correlation. We have created an interface for analyzing the datasets, which includes a novel visual artifact, the DTWRadar, for summarizing differences between time series. Using our tool we identify a number of behavioral properties that allow us to understand the predictive power of patterns of use.
Alberta, Saturday, May 12, 2007, 1:30pm to 3:00pm.