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Where and what- Using smartphones to predict next locations and applications in daily life

Where and what- Using smartphones to predict next locations and applications in daily life

Where and what- Using smartphones to predict next locations and applications in daily life

Where and what- Using smartphones to predict next locations and applications in daily life

locations and applications in daily life

Trinh Minh Tri Do a , ⇤, Daniel Gatica-Perez a , b

a

Idiap Research Institute, Switzerland b ÉcolePolytechnique Fédéralede Lausanne, Switzerland

a r t i c l e i n f o Article history:Received 10July 2012Received in revised form 16January 2013Accepted 18March 2013Available online 23April 2013Keywords:Smartphone data Human behavior Mobility prediction App usage prediction Lausanne Data Collection Campaign

a b s t r a c t

This paper investigates the prediction of two aspects of human behavior using smartphones as sensing devices. We present a framework for predicting where users will go and which app they will use in the next ten minutes by exploiting the rich contextual information from smartphone sensors. Our first goal is to understand which smartphone sensor data types are important for the two prediction tasks. Secondly, we aim at extracting generic (i.e.,user-independent) behavioral patterns and study how generic behavior models can improve the predictive performance of personalized models. Experimental validation was conducted on the Lausanne Data Collection Campaign (LDCC)dataset, with longitudinal smartphone data collected over a period of 17months from 71users.

©2013Elsevier B.V. All rights reserved. 1. Introduction

The ability to foresee what mobile users want to do has many applications such as improving user interfaces or providing relevant recommendations. For example, users can have quick access to the map application if the system knows that they are going to a new place and thus positioning assistance is needed. In another context, if the system knows that the user will go for lunch, then it can recommend a list of restaurants together with useful information like today’smenu, availabilities, traffic conditions, etc. Mobile phones are convenient options for tracking and mining user behavior in daily life [1, 2]as they are usually placed in close proximity to the users [3].Smartphones contain many sensors that can record contextual and activity cues, including location, application usage, and calling behavior. This information can be considered as both input and output of a prediction framework, in which the future values of some variables (e.g.,next place) are predicted based on the current context (currentplace, time, etc.). Constructing predictive models of human behavior has been a topic of interest in the area of recommendation systems [4–6],context-aware services [7, 8],and personalized and adaptive interfaces [9].While these studies support the claim that human behavior can be inferred through mobile phones, none of these studies, to our knowledge, exploit the information made available through multiple sensors such as location, call logs, and proximity to others in order to build or enhance predictive models capable of determining aspects of user behavior in the future. Hence, in this paper, we consider the task of predicting human behavior based on multiple smartphone sensors using statistical methods. Our framework is inspired from prediction algorithms commonly used in signal processing for forecasting time series. More precisely, we predict the next location of a user and which application he/shewill use based on the current context consisting of location, time, app usage, Bluetooth proximity, and communication logs. This approach allow
s modeling the interplay between the predicted variables to study relationships between the place where a user stays ⇤Corresponding author. Tel.:+41277217751.

The ability to foresee what mobile users want to do has many applications such as improving user interfaces or providing relevant recommendations. For example, users can have quick access to the map application if the system knows that they are going to a new place and thus positioning assistance is needed. In another context, if the system knows that the user will go for lunch, then it can recommend a list of restaurants together with useful information like today’smenu, availabilities, traffic conditions, etc. Mobile phones are convenient options for tracking and mining user behavior in daily life [1, 2]as they are usually placed in close proximity to the users [3].Smartphones contain many sensors that can record contextual and activity cues, including location, application usage, and calling behavior. This information can be considered as both input and output of a prediction framework, in which the future values of some variables (e.g.,next place) are predicted based on the current context (currentplace, time, etc.). Constructing predictive models of human behavior has been a topic of interest in the area of recommendation systems [4–6],context-aware services [7, 8],and personalized and adaptive interfaces [9].While these studies support the claim that human behavior can be inferred through mobile phones, none of these studies, to our knowledge, exploit the information made available through multiple sensors such as location, call logs, and proximity to others in order to build or enhance predictive models capable of determining aspects of user behavior in the future. Hence, in this paper, we consider the task of predicting human behavior based on multiple smartphone sensors using statistical methods. Our framework is inspired from prediction algorithms commonly used in signal processing for forecasting time series. More precisely, we predict the next location of a user and which application he/shewill use based on the current context consisting of location, time, app usage, Bluetooth proximity, and communication logs. This approach allows modeling the interplay between the predicted variables to study relationships between the place where a user stays ⇤Corresponding author. Tel.:+41277217751.

E-mail addresses:tri.do@idiap.ch, do@idiap.ch(T.M.T.Do), gatica@idiap.ch(D.Gatica-Perez).

1574-1192/$–see front matter ©2013Elsevier B.V. All rights reserved. http://www.doczj.com/doc/98466122bb4cf7ec4bfed059.html /10.1016/j.pmcj.2013.03.006

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