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.

Meeting Name

IEEE 16th International Conference on Data Mining, ICDM 2016 (2016: Dec. 12-15, Catelonia, Spain)


Computer Science


The research was supported in part by Natural Science Foundation of China (71329201). Yanjie Fu is the contact author.

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

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)

1550-4786; 2374-8486

Document Type

Article - Conference proceedings

Document Version


File Type





© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Dec 2017