Service usage analysis in mobile messaging apps: A multi-label multi-view perspective

Yanjie Fu, Junming Liu, Xiaolin Li, Xinjiang Lu, Jingci Ming, Chu Guan, Hui Xiong

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

7 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.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
EditorsFrancesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages877-882
Number of pages6
ISBN (Electronic)9781509054725
DOIs
StatePublished - Jul 2 2016
Event16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain
Duration: Dec 12 2016Dec 15 2016

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume0
ISSN (Print)1550-4786

Other

Other16th IEEE International Conference on Data Mining, ICDM 2016
Country/TerritorySpain
CityBarcelona, Catalonia
Period12/12/1612/15/16

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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