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
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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Sun, Dawen Kuang, Zhejun Xiong, Xingbo Zhao, Jian Wu, Gang Wang, Feng
AuthorAffiliation	1 College of Computer Science and Technology, Changchun University, Changchun 130022, China; kuangzhejun@ccu.edu.cn (Z.K.); 231501519@mails.ccu.edu.cn (X.X.); zhaojian@ccu.edu.cn (J.Z.) 2 Jilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement, Changchun 130022, China 3 Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130022, China 4 College of Computer Science and Technology, Jilin University, Changchun 130012, China; wugang17@mails.jlu.edu.cn (G.W.); wangfeng12@mails.jlu.edu.cn (F.W.)
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Cites_doi	10.1145/3477495.3532009 10.1609/aaai.v34i01.5330 10.1016/j.future.2021.11.006 10.1145/3377325.3377499 10.1007/978-3-030-64694-3_12 10.1007/978-981-99-4752-2_12 10.1109/INFOCOM42981.2021.9488687 10.1145/3576842.3582328 10.1016/j.engappai.2024.108766 10.1016/j.eswa.2023.121065 10.1016/j.ipm.2022.102869 10.1145/3485730.3494115 10.1109/SP40000.2020.00062 10.1145/3524610.3527922 10.1145/3540250.3558913 10.1145/3477495.3531937 10.1145/3397271.3401063 10.1145/3580305.3599768 10.1145/3432192 10.1145/3447264 10.1145/3404835.3462862 10.1145/3331184.3331267 10.1007/978-3-031-00126-0_11
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SubjectTerms	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
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Title	A Recommendation System for Trigger–Action Programming Rules via Graph Contrastive Learning
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