Unsupervised Clickstream Clustering for User Behavior Analysis
GANG WANG, Xinyi Zhang, Shiliang Tang, Haitao Zheng, Ben Y Zhao

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.

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