Modeling Users’ Behavior from Large Scale Smartphone Data Collection

Bhargava, Preeti and Agrawala, Ashok (2016) Modeling Users’ Behavior from Large Scale Smartphone Data Collection. EAI Endorsed Transactions on Context-aware Systems and Applications, 3 (10): e3. ISSN 2409-0026

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Abstract

A large volume of research in ubiquitous systems has been devoted to using data, that has been sensed from users’ smartphones, to infer their current high level context and activities. However, mining users’ diverse longitudinal behavioral patterns, which can enable exciting new context-aware applications, has not received much attention. In this paper, we focus on learning and identifying such behavioral patterns from large-scale data collected from users’ smartphones. To this end, we develop a unified infrastructure and implement several novel approaches for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and predicting their availability for accepting calls etc. We evaluate our work on real-world data of 200 users, from the DeviceAnalyzer dataset, consisting of 365 million data points and show that our algorithms and approaches can model user behavior with high accuracy and outperform existing approaches.

Item Type: Article
Uncontrolled Keywords: Context-aware computing and systems, User behavior modeling, Learning from context
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 16 Sep 2020 08:47
Last Modified: 16 Sep 2020 08:47
URI: https://eprints.eudl.eu/id/eprint/326

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