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.
%0 Journal Article
%1 Adar07
%A Adar, Eytan
%A Weld, Daniel
%A Bershad, Brian
%A Gribble, Steven
%B WWW 2007
%C Banff, Canada
%D 2007
%K datamining search time+series
%T Why We Search: Visualizing and Predicting User Behavior
%X 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.
@article{Adar07,
abstract = {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.},
added-at = {2007-05-18T10:17:37.000+0200},
address = {Banff, Canada},
author = {Adar, Eytan and Weld, Daniel and Bershad, Brian and Gribble, Steven},
biburl = {https://www.bibsonomy.org/bibtex/2f8d06832179f8ac6ea30e15f9cbfd64e/stefano},
booktitle = {WWW 2007},
interhash = {7e8b6fc57b9902ddfb09d8fdcdc5b2c8},
intrahash = {f8d06832179f8ac6ea30e15f9cbfd64e},
keywords = {datamining search time+series},
timestamp = {2007-05-18T10:17:37.000+0200},
title = {Why We Search: Visualizing and Predicting User Behavior},
year = 2007
}