Understanding and prediction of mobile application usage for smart phones

Shin, Choonsung; Hong, Jin-Hyuk; Dey, Anind K.

doi:10.1145/2370216.2370243

Cited by 239 publications

(202 citation statements)

References 14 publications

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“…Firstly, the studies on the usage analysis focus on understanding when and where apps are used in mobile phones [1]. The contextual usage patterns can then be leveraged for apps prediction (or recommendation) [8,10], which usually guides the development of adaptive user interfaces [5]. Moreover, a number of mobile recommender systems and target advertising engines have been designed based on the app usage analysis [9,13].…”

Section: Related Workmentioning

confidence: 99%

“…There are mainly two aspects of this interesting problem that the existing research has been done. On the one hand, the usage prediction and classification of mobile apps themselves [8,10,14], which can help users to search and launch apps efficiently. On the other hand, to exploit the app usage for developing other business intelligence services, such as recommendation, churn controlling, target advertising [13].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Replacement of Smartphones with Mobile App Usage

Yang

Wang

et al. 2016

Web Information Systems Engineering – WISE 2016

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Abstract. To identify right customers who intend to replace the smartphone can help to perform precision marketing and thus bring significant financial gains to cellphone retailers. In this paper, we provide a study of exploiting mobile app usage for predicting users who will change the phone in the future. We first analyze the characteristics of mobile log data and develop the temporal bag-of-apps model, which can transform the raw data to the app usage vectors. We then formularize the prediction problem, present the hazard based prediction model, and derive the inference procedure. Finally, we evaluate both data model and prediction model on real-world data. The experimental results show that the temporal usage data model can effectively capture the unique characteristics of mobile log data, and the hazard based prediction model is thus much more effective than traditional classification methods. Furthermore, the hazard model is explainable, that is, it can easily show how the replacement of smartphones relate to mobile app usage over time.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Replacement of Smartphones with Mobile App Usage

Yang

Wang

et al. 2016

Web Information Systems Engineering – WISE 2016

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“…To be spatially consistent, these techniques require all items to be displayed at once, which would make icons very small on a mobile device. Shin et al (2012) explored highlighting on the adaptive homescreen, by indicating the app with the largest increase in probability. This was found to be confusing for participants, especially when the highlighting was inaccurate.…”

Section: Stabilitymentioning

confidence: 99%

“…As a user installs more apps, the time and effort required to locate ones that do not feature on the homescreen will increase. Though only a small number of installed apps are used frequently (Falaki et al, 2010), the set that are frequently used changes over time (Shin, 2012). Therefore, the homescreen needs to be organised regularly.…”

Section: Introductionmentioning

confidence: 99%

Investigating UI Displacements in an Adaptive Mobile Homescreen

Norrie

Murray-Smith

2015

International Journal of Mobile Human Computer Interaction

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“…Several works such as Huang et al [15] and Shin et al [25] focus on predicting the next smartphone app that a user would use based on contextual information such as time, location, or usage information such as most frequently or recently used app. In contrast, we focus on determining the information and its source, from among several heterogeneous sources, that would prove most relevant to the user's situation irrespective of the source type -whether its an app or a service.…”

Section: Smartphone Application Usage Predictionmentioning

confidence: 99%

Bootstrapped Discovery and Ranking of Relevant Services and Information in Context-aware Systems

Bhargava¹,

Lampton²,

Agrawala³

2015

Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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A context-aware system uses context to provide relevant information and services to the user, where relevancy depends on the user's situation. This relevant information could include a wide range of heterogeneous content. Many existing context-aware systems determine this information based on pre-defined ontologies or rules. In addition, they rely on users' context history to filter it. Moreover, they often provide domain-specific information. Such systems are not applicable to a large and varied set of user situations and information needs, and may suffer from cold start for new users. In this paper, we address these limitations and propose a novel, general and flexible approach for bootstrapped discovery and ranking of heterogeneous relevant services and information in context-aware systems. We design and implement four variations of a base algorithm that ranks candidate relevant services, and the information to be retrieved from them, based on the semantic relatedness between the information provided by the services and the user's situation description. We conduct a live deployment with 14 subjects to evaluate the efficacy of our algorithms. We demonstrate that they have strong positive correlation with human supplied relevance rankings and can be used as an effective means to discover and rank relevant services and information. We also show that our approach is applicable to a wide set of users' situations and to new users without requiring any user interaction history.

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Understanding and prediction of mobile application usage for smart phones

Cited by 239 publications

References 14 publications

Predicting Replacement of Smartphones with Mobile App Usage

Predicting Replacement of Smartphones with Mobile App Usage

Investigating UI Displacements in an Adaptive Mobile Homescreen

Bootstrapped Discovery and Ranking of Relevant Services and Information in Context-aware Systems

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