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

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International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing (IEMAICLOUD 2021)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 273))

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 burden some 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 behavior and situational context. This has led to the development of a hybrid model based on both behavioral and situational context to detect the best time to send the notification to the user.

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Acknowledegment

Invest Northern Ireland is acknowledged for supporting this project under the Competence Centre Programs Grant RD0513853—Connected Health Innovation Centre.

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Authors and Affiliations

  1. School of Computing, Ulster University, Jordanstown, BT37 0QB, Northern Ireland, UK

    Rashid Kamal, Paul McCullagh, Ian Cleland & Chris Nugent

Authors
  1. Rashid Kamal
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  2. Paul McCullagh
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  3. Ian Cleland
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  4. Chris Nugent
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Corresponding author

Correspondence to Rashid Kamal .

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Editors and Affiliations

  1. University of Castilla-La Mancha (UCLM), Ciudad Real, Spain

    Fausto Pedro García Márquez

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Kamal, R., McCullagh, P., Cleland, I., Nugent, C. (2022). A Hybrid Model Based on Behavioural and Situational Context to Detect Best Time to Deliver Notifications on Mobile Devices. In: García Márquez, F.P. (eds) International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing. IEMAICLOUD 2021. Smart Innovation, Systems and Technologies, vol 273. Springer, Cham. https://doi.org/10.1007/978-3-030-92905-3_2

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