Identifying user clicks based on dependency graph

Jun Liu, Cheng Fang, Nirwan Ansari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2014 23rd Wireless and Optical Communication Conference, WOCC 2014
PublisherIEEE Computer Society
ISBN (Print)9781479952496
DOIs
StatePublished - 2014
Event2014 23rd Wireless and Optical Communication Conference, WOCC 2014 - Newark, NJ, United States
Duration: May 9 2014May 10 2014

Publication series

Name2014 23rd Wireless and Optical Communication Conference, WOCC 2014

Other

Other2014 23rd Wireless and Optical Communication Conference, WOCC 2014
Country/TerritoryUnited States
CityNewark, NJ
Period5/9/145/10/14

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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