TY - GEN
T1 - Identifying user clicks based on dependency graph
AU - Liu, Jun
AU - Fang, Cheng
AU - Ansari, Nirwan
PY - 2014
Y1 - 2014
N2 - Identifying user clicks from a large number of measured HTTP requests is the fundamental task for web usage mining, which is important for web administrators and developers. Nowadays, the prevalent parallel web browsing behavior caused by multi-tab web browsers renders accurate user click identification from massive requests a great challenge. In this paper, we propose a dependency graph model to describe the complicated web browsing behavior. Based on this model, we develop two algorithms to establish the dependency graph for measured requests, and identify user clicks by comparing their probabilities of being primary requests with a self-learned threshold. We evaluate our method with a large dataset collected from a real world mobile core network. The experimental results show that our method can achieve high accurate user clicks identification.
AB - Identifying user clicks from a large number of measured HTTP requests is the fundamental task for web usage mining, which is important for web administrators and developers. Nowadays, the prevalent parallel web browsing behavior caused by multi-tab web browsers renders accurate user click identification from massive requests a great challenge. In this paper, we propose a dependency graph model to describe the complicated web browsing behavior. Based on this model, we develop two algorithms to establish the dependency graph for measured requests, and identify user clicks by comparing their probabilities of being primary requests with a self-learned threshold. We evaluate our method with a large dataset collected from a real world mobile core network. The experimental results show that our method can achieve high accurate user clicks identification.
UR - http://www.scopus.com/inward/record.url?scp=84904196339&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904196339&partnerID=8YFLogxK
U2 - 10.1109/WOCC.2014.6839915
DO - 10.1109/WOCC.2014.6839915
M3 - Conference contribution
AN - SCOPUS:84904196339
SN - 9781479952496
T3 - 2014 23rd Wireless and Optical Communication Conference, WOCC 2014
BT - 2014 23rd Wireless and Optical Communication Conference, WOCC 2014
PB - IEEE Computer Society
T2 - 2014 23rd Wireless and Optical Communication Conference, WOCC 2014
Y2 - 9 May 2014 through 10 May 2014
ER -