A Recommendation System for Trigger–Action Programming Rules via Graph Contrastive Learning

Trigger–action programming (TAP) enables users to automate Internet of Things (IoT) devices by creating rules such as “IF Device1.TriggerState is triggered, THEN Device2.ActionState is executed”. As the number of IoT devices grows, the combination space between the functions provided by devices expa...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 24; číslo 18; s. 6151
Hlavní autoři:	Kuang, Zhejun, Xiong, Xingbo, Wu, Gang, Wang, Feng, Zhao, Jian, Sun, Dawen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 23.09.2024 MDPI
Témata:	Automation Collaboration Computational linguistics Efficiency End users graph contrastive learning Internet of Things Language processing Natural language interfaces Recommender systems rule recommendation Semantics trigger–action programming Web applications trigger–action programming rule recommendation graph contrastive learning Internet of Things
ISSN:	1424-8220, 1424-8220
On-line přístup:	Získat plný text
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