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

• URL: http://arxiv.org/abs/2101.03394v1
• Date: Sat, 9 Jan 2021 17:07:47 GMT
• Title: Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants
• Authors: Mohammad Aliannejadi and Hamed Zamani and Fabio Crestani and W. Bruce Croft
• Abstract summary: This paper addresses two research problems that are vital for developing effective personal mobile assistants: target apps selection and recommendation. Here we focus on context-aware models to leverage the rich contextual information available to mobile devices. We propose a family of context-aware neural models that take into account the sequential, temporal, and personal behavior of users.
• Score: 42.25496752260081
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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