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 Conference Paper
%1 adar2007
%A Adar, E.
%A Weld, D.
%A Bershad, B.
%A Gribble, S.
%B Proceedings of the 16th International World Wide Web Conference
%D 2007
%K behavior prediction search thema user visualization
%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.
@inproceedings{adar2007,
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 = {2015-10-20T12:46:23.000+0200},
author = {Adar, E. and Weld, D. and Bershad, B. and Gribble, S.},
biburl = {https://www.bibsonomy.org/bibtex/250d8126e702aec8de9269742fc696e5d/nosebrain},
booktitle = {Proceedings of the 16th International World Wide Web Conference},
interhash = {7e8b6fc57b9902ddfb09d8fdcdc5b2c8},
intrahash = {50d8126e702aec8de9269742fc696e5d},
keywords = {behavior prediction search thema user visualization},
timestamp = {2015-10-20T12:47:45.000+0200},
title = {Why We Search: Visualizing and Predicting User Behavior},
year = 2007
}