Revisitation analysis of smartphone app use

We present a revisitation analysis of smartphone use to investigate the question: do smartphones induce usage habits? We analysed three months of application launch logs from 165 users in naturalistic settings. Our analysis reveals distinct clusters of applications and users which share similar revisitation patterns. However, we show that much of smartphone usage on a macro-level is very similar to web browsing on desktops, and thus argue that smartphone usage is driven by innate service needs rather than technology characteristics. On the other hand, on a micro-level we identify unique characteristics in smartphone usage, and we present a rudimentary model that accounts for 92 % in the variability of our smartphone use. Author Keywords Revisitation, smartphone use, habits, user behaviou

Modeling Spatial Trajectories using Coarse-Grained Smartphone Logs

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 -- every user carries their smartphones wherever they go. In addition, with growing privacy concerns, users refrain from sharing their exact geographical coordinates and their social media activity. In this paper, we present REVAMP, a sequential POI recommendation approach that utilizes the user activity on smartphone applications (or apps) to identify their mobility preferences. This work aligns with the recent psychological studies of online urban users, which show that their spatial mobility behavior is largely influenced by the activity of their smartphone apps. In addition, our proposal of coarse-grained smartphone data refers to data logs collected in a privacy-conscious manner, i.e., consisting only of (a) category of the smartphone app and (b) category of check-in location. Thus, REVAMP is not privy to precise geo-coordinates, social networks, or the specific application being accessed. Buoyed by the efficacy of self-attention models, we learn the POI preferences of a user using two forms of positional encodings -- absolute and relative -- with each extracted from the inter-check-in dynamics in the check-in sequence of a user. Extensive experiments across two large-scale datasets from China show the predictive prowess of REVAMP and its ability to predict app- and POI categories.Comment: IEEE Transactions on Big Dat

Recommendations for enhancing consumer safe food management behaviour with smartphone technology

Addressing consumer food safety risks through transdisciplinary research efforts highlight the importance of leveraging the affordances of smartphone technology. However, existing smartphone apps are limited by having safe food management (SFM) information in silos, gaps in context-based user experience research and insufficient evidence that portrays comprehensive evaluation. This paper reports on a research, which aimed to investigate how the affordances of smartphone technology can be leveraged to enhance the provision of information and facilitate knowledge retention to improve SFM behaviours. The findings produce key recommendations for improving information campaigns that aim to enhance SFM behaviour. It reveals that emerging software design approaches should be leveraged while incorporating context-based design principles in apps for SFM information campaigns. It further reveals that consumers should be prompted with multiple cues to revisit SFM apps for knowledge reinforcement. Finally, it highlights the importance of a consumer-centric approach to the development of SFM information campaign

Evidence to support common application switching behaviour on smartphones

We find evidence to support common behaviour in smartphone usage based on analysis of application (app) switching. This is an overlooked aspect of smartphone usage that gives additional insight beyond screen time and the particular apps that are accessed. Using a dataset of usage behaviour from 53 participants over a six-week period, we find strong similarity in the structure of networks built from app switching, despite diversity in the apps used, and the volume of app switching. App switch networks exhibit small-world, broad-scale network features, with a rapid popularity decay, suggesting that preferential attachment may drive next-app decision-making

An Empirical Analysis of Internet Use on Smartphones: Characterizing Visit Patterns and User Differences

The original vision of ubiquitous computing was for computers to assist humans by providing subtle and fitting technologies in every environment. The iPhone and similar smartphones have provided continuous access to the internet to this end. In the current thesis, my goal was to characterize how the internet is used on smartphones to better understand what users do with technology away from the desktop. Naturalistic and longitudinal data were collected from iPhone users in the wild and analyzed to develop this understanding. Since there are two general ways to access the internet on smartphones—via native applications and a web browser—I describe usage patterns through each along with the influence of experience, the nature of the task and physical locations where smartphones were used on these patterns. The results reveal differences between technologies (the PC and the smartphone), platforms (native applications and the mobile browser), and users in how the internet was accessed. Findings indicate that longitudinal use of web browsers decreased sharply with time in favor of native application use, web page revisitation through browsers occurred very infrequently (approximately 25% of URLs are revisited by each user), bookmarks were used sparingly to access web content, physical location visitation followed patterns similar to virtual visitation on the internet, and Zipf distributions characterize mobile internet use. The web browser was not as central to smartphone use compared to the PC, but afforded certain types of activities such as searching and ad hoc browsing. In addition, users systematically differed from each other in how they accessed the internet suggesting different ways to support a wider spectrum of smartphone users

"What Apps Did You Use?": Understanding the Long-term Evolution of Mobile App Usage

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