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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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 24; no. 18; p. 6151
Main Authors:	Kuang, Zhejun, Xiong, Xingbo, Wu, Gang, Wang, Feng, Zhao, Jian, Sun, Dawen
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 23.09.2024 MDPI
Subjects:	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
Online Access:	Get full text
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