AppUsage2Vec: Modeling Smartphone App Usage for Prediction

Zhao, Sha; Luo, Zhiling; Jiang, Ziwen; Wang, Haiyan; Xu, Feng; Li, Shijian; Yin, Jianwei; Pan, Gang

doi:10.1109/icde.2019.00120

Cited by 46 publications

(33 citation statements)

References 29 publications

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“…According to the existing research on app prediction (Yan et al , 2012; Zhao et al , 2019), multi-dimensional cross-app behavior characteristic variables can be extracted from log data. At the same time, through the structured diary, this paper also obtained contextual information related to the users' cross-app behavior.…”

Section: Results Analysismentioning

confidence: 99%

“…Mobile apps have become an indispensable part of user's daily life nowadays, and users can decide which app to use according to their personal needs and interests (Zhao et al , 2019). In recent years, scholars have paid attention to predicting the next app that the user will use when interacting with the smartphone, thereby helping the user reduce the information system interaction burden.…”

Section: Related Workmentioning

confidence: 99%

“…Users often transfer from one app to another or other apps in one interaction session, called “transitions between apps” (Gouin-Vallerand et al , 2014). Zhao et al (2019) analyzed the log data of large-scale users and found that there are many app interaction sessions in the user's smartphone transaction log, and there are many behavioral data transferred from one app to other apps in these sessions.…”

Section: Related Workmentioning

confidence: 99%

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Characterizing and predicting the cross-app behavior in mobile search

Liang

2021

AJIM

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PurposeThis paper aims to explore the users' cross-app behavior characteristics in mobile search and to predict users' cross-app behavior using multi-dimensional information.Design/methodology/approachThis paper presents a longitudinal user experiment in 15 days. This paper recruited 30 participants and collected their mobile phone log data in the whole experiment. The structured diary method was also used to collect contextual information in mobile search.FindingsThis study focused on the users' cross-app behavior in mobile search and described cross-app behavior's basic characteristics. Usage of communication app and tool apps could trigger more cross-app behavior in mobile search. The method of cross-app behavior prediction in the mobile search was proposed. Collecting users' more contextual information, such as search tasks, search motivation and other environmental information, can effectively improve the prediction accuracy of cross-app behavior in mobile search.Practical implicationsThe future research on cross-app behavior prediction should focus on context information in mobile search. Better prediction of cross-app behavior can reduce the users' interaction burden.Originality/valueThis paper contributes to research into cross-app behavior, especially in the mobile search research domain.

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Section: Results Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Characterizing and predicting the cross-app behavior in mobile search

Liang

2021

AJIM

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“…According to Zhao et al [347], the battery energy consumption optimization can be done by predicting application usage in a smartphone. With the knowledge of the next application probably to be used, the battery energy consumption can be planned and optimized in advance to conserve the battery life of smartphones.…”

Section: ) Optimization Through Application Usage Patternmentioning

confidence: 99%

Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage

et al. 2019

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The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone's energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device's energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones' batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed.

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“…There have been some studies using smartphone apps to infer user personal information. For example, demographic attributes (e.g., gender, region and marital status), interests, personality traits and life stages have been learned from app lists installed on smartphones, app installation behaviors (installation, updating and uninstallation) and app usage behaviors (Chittaranjan et al 2011(Chittaranjan et al , 2013Frey et al 2015Frey et al , 2017Jesdabodi and Maalej 2015;Malmi and Weber 2016;Qin et al 2016;Rivron et al 2016;Seneviratne et al 2015;Tu et al 2019;Wang et al 2015Wang et al , 2018Xu et al 2011Xu et al , 2016bZhao et al 2016Zhao et al , 2017aZhao et al , b, c, 2018Zhao et al , 2019bLi et al 2015a;Mo et al 2012;Brdar et al 2012;Ying et al 2012;Andone et al 2016;Peltonen et al 2018;Zou et al 2013;Yu et al 2018;Ouyang et al 2018;Wang et al 2019;Böhmer et al 2011;Liu et al 2018). In this section, we will review the related work in three aspects: inferring demographics, explaining personality, and discovering life patterns.…”

Section: Related Workmentioning

confidence: 99%

Investigating smartphone user differences in their application usage behaviors: an empirical study

Zhao

et al. 2019

CCF Trans. Pervasive Comp. Interact.

Self Cite

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Smartphone applications (Abbr. apps) have become an indispensable part in our everyday lives. Users determine what apps to use depending on their personal needs and interests. Users with different attributes may have different needs, making it natural for their app usage behaviors to be different. The differences in app usage behaviors among users make it possible to infer their attributes. Knowing such differences could help improve mobile user experiences by enhancing smart services and devices. In this paper, we present an empirical study of investigating smartphone user differences on a large-scale dataset of recently used app lists from 106,672 Android users from China. We first investigate the user differences in app usage behaviors with respect to their attributes (gender, age, and income level). We find significant differences in app usage frequency, app usage with time context and functions. We then extract corresponding features from app usage records to infer the attributes of each user, and investigate the predictive ability of individual features and combinations of different individual features. We achieve the accuracy of 83.29% for gender, 69.94% for age (four age ranges) and 71.43% for income level (three income levels) with the best set of features, respectively. Finally, we discuss the implications of our findings and the limitations of this work.

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AppUsage2Vec: Modeling Smartphone App Usage for Prediction

Cited by 46 publications

References 29 publications

Characterizing and predicting the cross-app behavior in mobile search

Characterizing and predicting the cross-app behavior in mobile search

Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage

Investigating smartphone user differences in their application usage behaviors: an empirical study

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