Related papers: Smartphone App Usage Prediction Using Points of In…
We have carefully instrumented a large portion of the population living in a university graduate dormitory by giving participants Android smart phones running our sensing software. In this paper, we propose the novel problem of predicting…
The prevalence of smart mobile devices has promoted the popularity of mobile applications (a.k.a. apps). Supporting mobility has become a promising trend in software engineering research. This article presents an empirical study of…
When smartphones, applications (a.k.a, apps), and app stores have been widely adopted by the billions, an interesting debate emerges: whether and to what extent do device models influence the behaviors of their users? The answer to this…
There are around a hundred installed apps on an average smartphone. The high number of apps and the limited number of app icons that can be displayed on the device's screen requires a new paradigm to address their visibility to the user. In…
In recent years we have witnessed a shift towards personalized, context-based applications and services for mobile device users. A key component of many of these services is the ability to infer the current location and predict the future…
It is well known that learning customers' preference and making recommendations to them from today's information-exploded environment is critical and non-trivial in an on-line system. There are two different modes of recommendation systems,…
With the emergence of deep learning techniques, smartphone apps are now embedded on-device AI features for enabling advanced tasks like speech translation, to attract users and increase market competitiveness. A good interaction design is…
The value of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these measures is that they reflect actual usage at most indirectly. Indeed, analytic companies…
Explosion of number of smartphone apps and their diversity has created a fertile ground to study behaviour of smartphone users. Patterns of app usage, specifically types of apps and their duration are influenced by the state of the user and…
The Android ecosystem faces a notable challenge known as fragmentation, which denotes the extensive diversity within the system. This issue is mainly related to differences in system versions, device hardware specifications, and…
Prior works have shown that the list of apps installed by a user reveal a lot about user interests and behavior. These works rely on the semantics of the installed apps and show that various user traits could be learnt automatically using…
We are in the dawn of deep learning explosion for smartphones. To bridge the gap between research and practice, we present the first empirical study on 16,500 the most popular Android apps, demystifying how smartphone apps exploit deep…
With the increase in an average user's dependence on their mobile devices, the reliance on collecting his browsing history from mobile browsers has also increased. This browsing history is highly utilized in the advertising industry for…
Mobile devices have access to personal, potentially sensitive data, and there is a large number of mobile applications and third-party libraries that transmit this information over the network to remote servers (including app developer…
Singapore, an urbanized and populated country with high penetration of smartphones, provides an excellent base for citizen-centric participatory sensing applications. Mobile participatory sensing applications offer an efficient means of…
With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a…
Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications. The ability to accurately and extensively monitor and analyze these data is necessary. Much concern in mobile data analysis is…
Mobile phones enable the collection of a wealth of private information, from unique identifiers (e.g., email addresses), to a user's location, to their text messages. This information can be harvested by apps and sent to third parties,…
Socio-economic indicators provide context for assessing a country's overall condition. These indicators contain information about education, gender, poverty, employment, and other factors. Therefore, reliable and accurate information is…
Current approaches for points-of-interest (POI) recommendation learn the preferences of a user via the standard spatial features such as the POI coordinates, the social network, etc. These models ignore a crucial aspect of spatial mobility…