Online services are increasingly dependent on user participation. Whether it’s online social networks or crowdsourcing services, understanding user behavior is important yet challenging. In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users’ click events), and visualize the detected behaviors in an intuitive manner. Our system identifies
“clusters” of similar users by partitioning a similarity graph
(nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviors. For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks. Our system effectively identifies
previously unknown behaviors, e.g., dormant users, hostile chatters. Also, our user study shows people can easily interpret identified behaviors using our visualization tool
%0 Conference Paper
%1 conf/chi/WangZTZZ16
%A Wang, Gang
%A Zhang, Xinyi
%A Tang, Shiliang
%A Zheng, Haitao
%A Zhao, Ben Y.
%B CHI
%D 2016
%E Kaye, Jofish
%E Druin, Allison
%E Lampe, Cliff
%E Morris, Dan
%E Hourcade, Juan Pablo
%I ACM
%K final thema:clickstream_clustering
%P 225-236
%T Unsupervised Clickstream Clustering for User Behavior Analysis.
%U http://dblp.uni-trier.de/db/conf/chi/chi2016.html#WangZTZZ16
%X Online services are increasingly dependent on user participation. Whether it’s online social networks or crowdsourcing services, understanding user behavior is important yet challenging. In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users’ click events), and visualize the detected behaviors in an intuitive manner. Our system identifies
“clusters” of similar users by partitioning a similarity graph
(nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviors. For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks. Our system effectively identifies
previously unknown behaviors, e.g., dormant users, hostile chatters. Also, our user study shows people can easily interpret identified behaviors using our visualization tool
%@ 978-1-4503-3362-7
@inproceedings{conf/chi/WangZTZZ16,
abstract = {Online services are increasingly dependent on user participation. Whether it’s online social networks or crowdsourcing services, understanding user behavior is important yet challenging. In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users’ click events), and visualize the detected behaviors in an intuitive manner. Our system identifies
“clusters” of similar users by partitioning a similarity graph
(nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviors. For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks. Our system effectively identifies
previously unknown behaviors, e.g., dormant users, hostile chatters. Also, our user study shows people can easily interpret identified behaviors using our visualization tool},
added-at = {2018-11-27T11:40:30.000+0100},
author = {Wang, Gang and Zhang, Xinyi and Tang, Shiliang and Zheng, Haitao and Zhao, Ben Y.},
biburl = {https://www.bibsonomy.org/bibtex/2545266060c7498f54e1ddfa4175a1ff8/m.meissner},
booktitle = {CHI},
crossref = {conf/chi/2016},
editor = {Kaye, Jofish and Druin, Allison and Lampe, Cliff and Morris, Dan and Hourcade, Juan Pablo},
ee = {https://doi.org/10.1145/2858036.2858107},
interhash = {5c722cca39a424d184792c0038ae4014},
intrahash = {545266060c7498f54e1ddfa4175a1ff8},
isbn = {978-1-4503-3362-7},
keywords = {final thema:clickstream_clustering},
pages = {225-236},
publisher = {ACM},
timestamp = {2018-11-27T12:25:04.000+0100},
title = {Unsupervised Clickstream Clustering for User Behavior Analysis.},
url = {http://dblp.uni-trier.de/db/conf/chi/chi2016.html#WangZTZZ16},
year = 2016
}