Unsupervised Clickstream Clustering For User Behavior Analysis
Gang Wang
Xinyi Zhang
Shiliang Tang
Haitao Zheng
Ben Y. Zhao
Proc. of the 34th CHI Conference on Human Factors in Computing Systems (CHI 2016)
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Paper 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.