Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants

TL;DR

This paper develops context-aware neural models for selecting and recommending target apps on smartphones, leveraging rich sensor data to improve personal mobile assistant functionalities and user experience.

Contribution

It introduces novel context-aware neural models for app selection and recommendation, utilizing a new dataset with sensor data to enhance mobile assistant capabilities.

Findings

Proposed models outperform baselines significantly.

Collected and made publicly available a large dataset of mobile queries with sensor data.

Demonstrated the importance of context in app prediction tasks.

Abstract

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 paper 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.