Service Usage Analysis in Mobile Messaging Apps: A Multi-Label Multi-View Perspective
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.
Y. Fu et al., "Service Usage Analysis in Mobile Messaging Apps: A Multi-Label Multi-View Perspective," Proceedings of the IEEE 16th International Conference on Data Mining (2016, Catolonia, Spain), pp. 877-882, Institute of Electrical and Electronics Engineers (IEEE), Dec 2017.
The definitive version is available at https://doi.org/10.1109/ICDM.2016.0106
IEEE 16th International Conference on Data Mining, ICDM 2016 (2016: Dec. 12-15, Catelonia, Spain)
Keywords and Phrases
Alignment; Time delay; Classification accuracy; Classification consistency; Classification methods; Mobile messaging; Multi-view learning; Post classification; Real-world datasets; Segmentation methods; Data mining
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International Standard Serial Number (ISSN)
Article - Conference proceedings
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