Recommendations for creating trigger-action rules in a block-based environment

Given the growing adoption of Internet of Things (IoT) technologies, several approaches have been presented to enable people to increase their control over their smart devices and provide relevant support. Recommendation systems have been proposed in many domains, but have received limited attention...

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Bibliographic Details
Published in:Behaviour & information technology Vol. 40; no. 10; pp. 1024 - 1034
Main Authors: Mattioli, Andrea, Paternò, Fabio
Format: Journal Article
Language:English
Published: London Taylor & Francis 27.07.2021
Taylor & Francis Ltd
Subjects:
Adoption of innovations
Editing
Electronic devices
End user development
Internet
Internet of Things
recommendation systems
recommendations for personalisation
Recommender systems
Sequences
trigger-action programming
ISSN:0144-929X, 1362-3001
Online Access:Get full text
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Summary:Given the growing adoption of Internet of Things (IoT) technologies, several approaches have been presented to enable people to increase their control over their smart devices and provide relevant support. Recommendation systems have been proposed in many domains, but have received limited attention in the area of End-User Development (EUD). We propose a novel approach for formulating recommendations in this area, based on deconstructing trigger-action rules into sequences of elements and the links between them. For this purpose, we propose a solution inspired by methods aimed at addressing the sequence-prediction problem. We have used this approach to provide users with two different types of recommendations: full rules for the one being edited, and parts of rules relevant for the next step to take in order to complete the current rule editing. In this paper, we present the design and a first evaluation of the two different possibilities to generate and display recommendations in a block-based EUD environment for creating automations for IoT contexts.
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ISSN:0144-929X
1362-3001
DOI:10.1080/0144929X.2021.1900396