Our preliminary study reveals that individuals use various management strategies for limiting smartphone use, ranging from keeping smartphones out of reach to removing apps. However, we also found that users often had difficulties in maintaining their chosen management strategies due to lack of self-regulation. In this paper, we present NUGU, a group-based intervention app for improving self-regulation of limiting smartphone use through leveraging social support: groups of people limit their use together by sharing their limiting information. NUGU is designed based on social cognitive theory, and it has been developed iteratively through two pilot tests. Our three-week user study (n = 62) demonstrated that compared with its non-social counterpart, the NUGU users' usage amount significantly decreased and their perceived level of managing disturbances improved. Furthermore, our exit interview confirmed that NUGU's design elements are effective for achieving limiting goals.

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NUGU: A Group-based Intervention App for Improving Self-Regulation of Limiting Smartphone Use

Over the last decade, the renaissance of Web technologies has transformed the online world into an application (App) driven society. While the abundant Apps have provided great convenience, their sheer number also leads to severe information overload, making it difficult for users to identify desired Apps. To alleviate the information overloading issue, recommender systems have been proposed and deployed for the App domain. However, existing work on App recommendation has largely focused on one single platform (e.g., smartphones), while it ignores the rich data of other relevant platforms (e.g., tablets and computers).In this article, we tackle the problem of cross-platform App recommendation, aiming at leveraging users’ and Apps’ data on multiple platforms to enhance the recommendation accuracy. The key advantage of our proposal is that by leveraging multiplatform data, the perpetual issues in personalized recommender systems—data sparsity and cold-start—can be largely alleviated. To this end, we propose a hybrid solution, STAR (short for “croSs-plaTform App Recommendation”) that integrates both numerical ratings and textual content from multiple platforms. In STAR, we innovatively represent an App as an aggregation of common features across platforms (e.g., App’s functionalities) and specific features that are dependent on the resided platform. In light of this, STAR can discriminate a user’s preference on an App by separating the user’s interest into two parts (either in the App’s inherent factors or platform-aware features). To evaluate our proposal, we construct two real-world datasets that are crawled from the App stores of iPhone, iPad, and iMac. Through extensive experiments, we show that our STAR method consistently outperforms highly competitive recommendation methods, justifying the rationality of our cross-platform App recommendation proposal and the effectiveness of our solution.

Cross-Platform App Recommendation by Jointly Modeling Ratings and Texts

Mining smartphone data for app usage prediction and recommendations: A survey

We consider participatory parental mediation in which children engage with their parents in activities that encourage both parents and children to participate in co-learning of digital media use. To this end, we developed FamiLync, a mobile service that treats use-limiting as a family activity and provides the family with a virtual public space to foster social awareness and improve self-regulation. A three-week user study conducted with twelve families in Korea (17 parents and 18 teenagers) showed that FamiLync improves mutual understanding of usage behavior, thereby providing common grounds for parental mediation. Further, parents actively participated in use-limiting with their children, which significantly increased the children's desire to participate. As a consequence, parental mediation methods and parent-child interaction in relation to smartphone usage changed appreciably, and the participants smartphone usage amount significantly decreased.

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FamiLync: facilitating participatory parental mediation of adolescents' smartphone use

Prior studies have addressed many negative aspects of mobile distractions in group activities. In this paper, we present Lock n' LoL. This is an application designed to help users focus on their group activities by allowing group members to limit their smartphone usage together. In particular, it provides synchronous social awareness of each other's limiting behavior. This synchronous social awareness can arouse feelings of connectedness among group members and can mitigate social vulnerability due to smartphone distraction (e.g., social exclusion) that often results in poor social experiences. After following an iterative prototyping process, we conducted a large-scale user study (n = 976) via real field deployment. The study results revealed how the participants used Lock n' LoL in their diverse contexts and how Lock n' LoL helped them to mitigate smartphone distractions.

/pdf/lock-n-lol-group-based-limiting-assistance-app-to-mitigate-3dm6cuv7sv.pdf

Lock n' LoL: Group-based Limiting Assistance App to Mitigate Smartphone Distractions in Group Activities

With the increasing adoption of smartphones also a problematic phenomena become apparent: People are changing their habits and become addicted to different services that these devices provide. In this paper we present AppDetox: an app that allows users to purposely create rules that keep them from using certain apps. We describe our deployment of the app on a mobile application store, and present initial findings gained through observation of about 11,700 users of the application. We find that people are rather rigorous when restricting their app use, and that mostly they suppress use of social networking and messaging apps.

AppDetox: helping users with mobile app addiction

Mobile phones have evolved from communication to multi-purpose devices that assist people with applications in various contexts and tasks. The size of the mobile ecosystem is steadily growing and new applications become available every day. This increasing number of applications makes it difficult for end-users to find good applications. Recommender systems suggesting mobile applications are being built to help people to find valuable applications. Since the nature of mobile applications differs from classical items to be recommended (e.g. books, movies, other goods), not only can new approaches for recommendation be developed, but also new paradigms for evaluating performance of recommender systems are advisable. During the lifecycle of mobile applications, different events can be observed that provide insights into users' engagement with particular applications. This gives rise to new approaches for evaluation of recommender systems. In this paper, we present AppFunnel: a framework that allows for usage-centric evaluation considering different stages of application engagement. We present a case study and discuss capabilities for evaluating recommender engines by applying metrics to the AppFunnel.

Lyubomir Ganev

Papers

AppDetox: helping users with mobile app addiction

AppFunnel: a framework for usage-centric evaluation of recommender systems that suggest mobile applications