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

Authors:

Mohammad Aliannejadi,

Fabio Crestani,

W. Bruce CroftAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 39, Issue 3

Article No.: 29, Pages 1 - 30

https://doi.org/10.1145/3447678

Published: 05 May 2021 Publication History

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 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.

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Cited By

Khaokaew YXue HSalim F(2024)MAPLEProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435148:1(1-25)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643514
Fan XWu ZYu CRao FShi WTu T(2024)ContextCam: Bridging Context Awareness with Creative Human-AI Image Co-CreationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642129(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642129
Wang CWu H(2024)Energy Optimization for Federated Learning on Consumer Mobile Devices With Asynchronous SGD and Application Co-ExecutionIEEE Transactions on Mobile Computing10.1109/TMC.2024.337923623:11(10235-10250)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TMC.2024.3379236
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Index Terms

Context-aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. Field studies
  2. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices
      1. Personal digital assistants
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 39, Issue 3

July 2021

432 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3450607

Editor:
Min Zhang
Tsinghua University, China

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Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 05 May 2021

Accepted: 01 January 2021

Revised: 01 January 2021

Received: 01 April 2020

Published in TOIS Volume 39, Issue 3

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Cited By

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https://dl.acm.org/doi/10.1145/3643514
Fan XWu ZYu CRao FShi WTu T(2024)ContextCam: Bridging Context Awareness with Creative Human-AI Image Co-CreationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642129(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642129
Wang CWu H(2024)Energy Optimization for Federated Learning on Consumer Mobile Devices With Asynchronous SGD and Application Co-ExecutionIEEE Transactions on Mobile Computing10.1109/TMC.2024.337923623:11(10235-10250)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TMC.2024.3379236
Saiapin ABalitskiy GBershatsky DKatrutsa AFrolov EFrolov AOseledets IKharin V(2024)Federated privacy-preserving collaborative filtering for on-device next app predictionUser Modeling and User-Adapted Interaction10.1007/s11257-024-09395-034:4(1369-1398)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11257-024-09395-0
Wu YCao JXu G(2023)Fairness in Recommender Systems: Evaluation Approaches and Assurance StrategiesACM Transactions on Knowledge Discovery from Data10.1145/360455818:1(1-37)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3604558
Cai DQian SFang QHu JXu C(2023)User Cold-Start Recommendation via Inductive Heterogeneous Graph Neural NetworkACM Transactions on Information Systems10.1145/356048741:3(1-27)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3560487
Boyd AShowalter SMandt SSmyth PKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Predictive querying for autoregressive neural sequence modelsProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601995(23751-23764)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3601995
Luo SZhang XXiao YSong LAl Hasan MXiong L(2022)HySAGEProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557354(1389-1398)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557354

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