Nihao: A Predictive Smartphone Application Launcher

Abstract

Abstract. Increasingly large number of the applications installed on smartphones tends to harm the application lookup efficiency. In this paper, we introduce Nihao, a personalized intelligent app launcher system, which could help the users to find apps quickly. Nihao predicts which app the user will likely open next based on a Bayesian Network model leveraging the contextual information such as the time of day, the day of week, the user's location and the last used app with the hypothesis that the users' app usage pattern is context dependent. Through the field study with seven users over six weeks, we first validate the above hypothesis by comparing the prediction accuracy of Nihao with other predictors. We found that the larger UI change did not necessarily yield longer app lookup time as the app lookup time highly depended on the app icon position on screen, which suggested the prediction accuracy was the most important factor in designing such a system. At the end of the study, we conducted a user survey to evaluate Nihao qualitatively. The survey results show that five out of seven users were quite satisfied with the prediction of Nihao and thought it could help to save both app lookup and management time by ranking the app icons automatically while Nihao did not help the other two users much since they used their phones primarily for calling and texting (not for apps).

CrowdRecruiter

“…For instance, executing sensing tasks in parallel with Google Map usage also reduces energy consumption when performing PCS tasks [4]. We plan to study the participant selection that leverages multiple piggyback sensing opportunities in a holistic manner as many predictive models, such as for app usage [42], already exist. Different Incentive Payment Models: In this paper we adopt the payment model where each participant receives a fixed amount of incentive through the whole task period.…”

Cap

“…Prior works have attempted to predict mobile App usage [29,32,35]. Church et al [24] summarized the challenges for mobile phone usage learning and analysis as well as a series of studies and applications on mobile phone usage, including App recommendation [88], launcher prediction [63], and battery management [31]. Various prediction algorithms have been explored to achieve that goal.…”

Revisitation analysis of smartphone app use

“…In terms of habits (i.e. patterns, routines) in smartphone application use, previous work has sought to model and predict which applications people are likely to install [37], use [31], and for how long [13].…”