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Machine Learning Techniques for Mobile Application Event Analysis

Authors:
Ben Falchuk
Shoshana Loeb
Chris Mesterharm
Euthimios Panagos

Keywords: Machine Learning; Analytics; Big Data; Mobile Apps; Data Clustering

Abstract:
As increasing amounts of economic, entertainment and social activities are occurring using native and web applications, it has become essential for developers to analyze user interactions in order to better understand their behavior and increase engagement and monetization. In this paper, we describe how JumpStart, a real-time event analytics service, utilizes machine learning techniques for empowering developers and businesses to both identify users exhibiting similar behavior and discover user interaction patterns that are strongly correlated with specific activities (e.g., purchases). Discovered interaction patterns can be used for enabling contextual real-time feedback via JumpStart’s complex event pattern matching.

Pages: 50 to 55

Copyright: Copyright (c) IARIA, 2016

Publication date: October 9, 2016

Published in: conference

ISSN: 2326-9383

ISBN: 978-1-61208-509-8

Location: Venice, Italy

Dates: from October 9, 2016 to October 13, 2016