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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Vydáno v:	Behaviour & information technology Ročník 40; číslo 10; s. 1024 - 1034
Hlavní autoři:	Mattioli, Andrea, Paternò, Fabio
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Taylor & Francis 27.07.2021 Taylor & Francis Ltd
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0144-929X 1362-3001
DOI:	10.1080/0144929X.2021.1900396