A Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices

Rashid Kamal; Paul McCullagh; I Cleland; CD Nugent

doi:10.1007/978-3-030-92905-3_2

A Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices

Rashid Kamal, Paul McCullagh, I Cleland, CD Nugent

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Notifications received on the smartphone have become a vital part of typical work and social life. Notifications provide information ranging from important incoming business emails and meeting prompts to home deliveries and social interaction. But they can also become burdensome on the user as the average number of incoming notifications can be in the region of 100 per day. To cope with this overload a user might completely disable the notifications which will result in missing important information. This paper evaluates the user challenges, methods used to manage these challenges and discusses key challenges for data collection. In particular, we assess an overlap between user behaviour and situational context. This has led to the development of a hybrid model based on both behavioural and situational context to detect the best time to send the notification to the user.

Original language	English
Title of host publication	Proceedings of International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing
Publisher	Springer Cham
Pages	11-21
Number of pages	11
ISBN (Electronic)	978-3-030-92905-3
ISBN (Print)	978-3-030-92904-6
DOIs	https://doi.org/10.1007/978-3-030-92905-3_2
Publication status	Published online - 5 May 2022
Event	International Conference on Integrated Emerging Methods of Artificial Intelligence & Cloud Computing - Virtual Mode, London, United Kingdom Duration: 26 Apr 2021 → 29 Apr 2021 Conference number: 2021 https://iemaicloud.org/

Publication series

Name	Smart Innovation, Systems and Technologies book series
Volume	273
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	International Conference on Integrated Emerging Methods of Artificial Intelligence & Cloud Computing
Abbreviated title	IEMAICLOUD
Country/Territory	United Kingdom
City	London
Period	26/04/21 → 29/04/21
Internet address	https://iemaicloud.org/

Keywords

Ubiquitous computing
Hybrid Model
Notification Management System
Machine Learning (ML)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-92905-3_2

Cite this

Kamal, R., McCullagh, P., Cleland, I., & Nugent, CD. (2022). A Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices. In Proceedings of International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing (pp. 11-21). (Smart Innovation, Systems and Technologies book series; Vol. 273). Springer Cham. Advance online publication. https://doi.org/10.1007/978-3-030-92905-3_2

Kamal, Rashid ; McCullagh, Paul ; Cleland, I et al. / A Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices. Proceedings of International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing. Springer Cham, 2022. pp. 11-21 (Smart Innovation, Systems and Technologies book series).

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title = "A Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices",

abstract = "Notifications received on the smartphone have become a vital part of typical work and social life. Notifications provide information ranging from important incoming business emails and meeting prompts to home deliveries and social interaction. But they can also become burdensome on the user as the average number of incoming notifications can be in the region of 100 per day. To cope with this overload a user might completely disable the notifications which will result in missing important information. This paper evaluates the user challenges, methods used to manage these challenges and discusses key challenges for data collection. In particular, we assess an overlap between user behaviour and situational context. This has led to the development of a hybrid model based on both behavioural and situational context to detect the best time to send the notification to the user.",

keywords = "Ubiquitous computing, Hybrid Model, Notification Management System, Machine Learning (ML)",

author = "Rashid Kamal and Paul McCullagh and I Cleland and CD Nugent",

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booktitle = "Proceedings of International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing",

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Kamal, R, McCullagh, P, Cleland, I & Nugent, CD 2022, A Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices. in Proceedings of International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing. Smart Innovation, Systems and Technologies book series, vol. 273, Springer Cham, pp. 11-21, International Conference on Integrated Emerging Methods of Artificial Intelligence & Cloud Computing, London, United Kingdom, 26/04/21. https://doi.org/10.1007/978-3-030-92905-3_2

A Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices. / Kamal, Rashid; McCullagh, Paul; Cleland, I et al.
Proceedings of International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing. Springer Cham, 2022. p. 11-21 (Smart Innovation, Systems and Technologies book series; Vol. 273).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices

AU - Kamal, Rashid

AU - McCullagh, Paul

AU - Cleland, I

AU - Nugent, CD

N1 - Conference code: 2021

PY - 2022/5/5

Y1 - 2022/5/5

N2 - Notifications received on the smartphone have become a vital part of typical work and social life. Notifications provide information ranging from important incoming business emails and meeting prompts to home deliveries and social interaction. But they can also become burdensome on the user as the average number of incoming notifications can be in the region of 100 per day. To cope with this overload a user might completely disable the notifications which will result in missing important information. This paper evaluates the user challenges, methods used to manage these challenges and discusses key challenges for data collection. In particular, we assess an overlap between user behaviour and situational context. This has led to the development of a hybrid model based on both behavioural and situational context to detect the best time to send the notification to the user.

AB - Notifications received on the smartphone have become a vital part of typical work and social life. Notifications provide information ranging from important incoming business emails and meeting prompts to home deliveries and social interaction. But they can also become burdensome on the user as the average number of incoming notifications can be in the region of 100 per day. To cope with this overload a user might completely disable the notifications which will result in missing important information. This paper evaluates the user challenges, methods used to manage these challenges and discusses key challenges for data collection. In particular, we assess an overlap between user behaviour and situational context. This has led to the development of a hybrid model based on both behavioural and situational context to detect the best time to send the notification to the user.

KW - Ubiquitous computing

KW - Hybrid Model

KW - Notification Management System

KW - Machine Learning (ML)

U2 - 10.1007/978-3-030-92905-3_2

DO - 10.1007/978-3-030-92905-3_2

M3 - Conference contribution

SN - 978-3-030-92904-6

T3 - Smart Innovation, Systems and Technologies book series

SP - 11

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BT - Proceedings of International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing

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T2 - International Conference on Integrated Emerging Methods of Artificial Intelligence & Cloud Computing

Y2 - 26 April 2021 through 29 April 2021

ER -

Kamal R, McCullagh P, Cleland I , Nugent CD. A Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices. In Proceedings of International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing. Springer Cham. 2022. p. 11-21. (Smart Innovation, Systems and Technologies book series). Epub 2022 May 5. doi: 10.1007/978-3-030-92905-3_2

A Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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Cite this