A multi-label multi-view learning framework for in-app service usage analysis

Yanjie Fu, Junming Liu, Xiaolin Li, Hui Xiong

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The service usage analysis, aiming at identifying customers' messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a traffic sequence into single-usage subsequences, and then classify the subsequences into different usage types. However, they could suffer from inaccurate traffic segmentations and mixed-usage subsequences. To address this challenge, we exploit a multi-label multi-view learning strategy and develop an enhanced framework for in-App usage analytics. Specifically, we first devise an enhanced traffic segmentation method to reduce mixed-usage subsequences. Besides, we develop a multi-label multi-view logistic classification method, which comprises two alignments. The first alignment is to make use of the classification consistency between packet-length view and time-delay view of traffic subsequences and improve classification accuracy. The second alignment is to combine the classification of single-usage subsequence and the post-classification of mixed-usage subsequences into a unified multi-label logistic classification problem. Finally, we present extensive experiments with real-world datasets to demonstrate the effectiveness of our approach. We find that the proposed multi-label multi-view framework can help overcome the pain of mixedusage subsequences and can be generalized to latent activity analysis in sequential data, beyond in-App usage analytics.

Original languageEnglish (US)
Article numbera40
JournalACM Transactions on Intelligent Systems and Technology
Volume9
Issue number4
DOIs
StatePublished - Jan 2018

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Artificial Intelligence

Keywords

  • In-app analytics
  • Internet traffic
  • Multi-label
  • Multi-view
  • Service usage

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