Identifying in-app user actions from mobile web logs

Priyogi, B, Sanderson, M, Salim, F, Chan, J, Tomko, M and Ren, Y 2018, 'Identifying in-app user actions from mobile web logs', in Dinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi (ed.) Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018) Part II, Melbourne, Australia, 3-6 June 2018, pp. 300-311.


Document type: Conference Paper
Collection: Conference Papers

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Title Identifying in-app user actions from mobile web logs
Author(s) Priyogi, B
Sanderson, M
Salim, F
Chan, J
Tomko, M
Ren, Y
Year 2018
Conference name PAKDD 2018: Lecture Notes in Artificial Intelligence Volume 10938
Conference location Melbourne, Australia
Conference dates 3-6 June 2018
Proceedings title Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018) Part II
Editor(s) Dinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi
Publisher Springer
Place of publication Switzerland
Start page 300
End page 311
Total pages 12
Abstract We address the problem of identifying in-app user actions from Web access logs when the content of those logs is both encrypted (through HTTPS) and also contains automated Web accesses. We find that the distribution of time gaps between HTTPS accesses can distinguish user actions from automated Web accesses generated by the apps, and we determine that it is reasonable to identify meaningful user actions within mobile Web logs by modelling this temporal feature. A real-world experiment is conducted with multiple mobile devices running some popular apps, and the results show that the proposed clustering-based method achieves good accuracy in identifying user actions, and outperforms the state-of-the-art baseline by 17.84%.
Subjects Information Retrieval and Web Search
Keyword(s) Transaction identification
Mobile Web logs
DOI - identifier 10.1007/978-3-319-93037-4_24
Copyright notice © Springer International Publishing AG, part of Springer Nature 2018
ISBN 9783319930367
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