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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Summary: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 expands, making manual rule creation time-consuming for end-users. Existing TAP recommendation systems enhance the efficiency of rule discovery but face two primary issues: they ignore the association of rules between users and fail to model collaborative information among users. To address these issues, this article proposes a graph contrastive learning-based recommendation system for TAP rules, named GCL4TAP. In GCL4TAP, we first devise a data partitioning method called DATA2DIV, which establishes cross-user rule relationships and is represented by a user–rule bipartite graph. Then, we design a user–user graph to model the similarities among users based on the categories and quantities of devices that they own. Finally, these graphs are converted into low-dimensional vector representations of users and rules using graph contrastive learning techniques. Extensive experiments conducted on a real-world smart home dataset demonstrate the superior performance of GCL4TAP compared to other state-of-the-art methods.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24186151