Users install many apps on their smartphones, raising issues related to information overload for users and resource management for devices. Moreover, the recent increase in the use of personal assistants has made mobile devices even more pervasive in users’ lives. This article addresses two research problems that are vital for developing effective personal mobile assistants: target apps selection and recommendation . The former is the key component of a unified mobile search system: a system that addresses the users’ information needs for all the apps installed on their devices with a unified mode of access. The latter, instead, predicts the next apps that the users would want to launch. Here we focus on context-aware models to leverage the rich contextual information available to mobile devices. We design an in situ study to collect thousands of mobile queries enriched with mobile sensor data (now publicly available for research purposes). With the aid of this dataset, we study the user behavior in the context of these tasks and propose a family of context-aware neural models that take into account the sequential, temporal, and personal behavior of users. We study several state-of-the-art models and show that the proposed models significantly outperform the baselines.

中文翻译：

---

增强个人移动助理的上下文感知目标应用选择和推荐

用户在其智能手机上安装了许多应用程序，从而引发了与用户信息过载和设备资源管理相关的问题。此外，最近个人助理使用的增加使移动设备在用户的生活中更加普遍。本文解决了两个对于开发有效的个人移动助理至关重要的研究问题：目标应用选择和推荐. 前者是统一移动搜索系统的关键组成部分：该系统以统一的访问模式满足用户对其设备上安装的所有应用程序的信息需求。相反，后者预测用户想要启动的下一个应用程序。在这里，我们专注于上下文感知模型，以利用移动设备可用的丰富上下文信息。我们设计一个原位研究收集数千个移动传感器数据丰富的移动查询（现已公开用于研究目的）。借助该数据集，我们研究了这些任务上下文中的用户行为，并提出了一系列考虑到用户的顺序、时间和个人行为的上下文感知神经模型。我们研究了几个最先进的模型，并表明所提出的模型明显优于基线。