Service Usage Analysis in Mobile Messaging Apps: A Multi-Label Multi-View Perspective
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.
Recommended Citation
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
Meeting Name
IEEE 16th International Conference on Data Mining, ICDM 2016 (2016: Dec. 12-15, Catelonia, Spain)
Department(s)
Computer Science
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)
978-1-5090-5473-2
International Standard Serial Number (ISSN)
1550-4786; 2374-8486
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Dec 2017
Comments
The research was supported in part by Natural Science Foundation of China (71329201). Yanjie Fu is the contact author.