{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:16:25Z","timestamp":1771467385491,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Project of Jilin Provincial Department of Education","award":["JJKH20230680KJ"],"award-info":[{"award-number":["JJKH20230680KJ"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Trigger\u2013action programming (TAP) enables users to automate Internet of Things (IoT) devices by creating rules such as \u201cIF Device1.TriggerState is triggered, THEN Device2.ActionState is executed\u201d. 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\u2013rule bipartite graph. Then, we design a user\u2013user 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.<\/jats:p>","DOI":"10.3390\/s24186151","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"6151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Recommendation System for Trigger\u2013Action Programming Rules via Graph Contrastive Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5632-7596","authenticated-orcid":false,"given":"Zhejun","family":"Kuang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"},{"name":"Jilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement, Changchun 130022, China"},{"name":"Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingbo","family":"Xiong","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"},{"name":"Jilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement, Changchun 130022, China"},{"name":"Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3265-6461","authenticated-orcid":false,"given":"Jian","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"},{"name":"Jilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement, Changchun 130022, China"},{"name":"Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawen","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"},{"name":"Jilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement, Changchun 130022, China"},{"name":"Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121065","DOI":"10.1016\/j.eswa.2023.121065","article-title":"A data fusion framework based on heterogeneous information network embedding for trigger-action programming in IoT","volume":"235","author":"Wu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3447264","article-title":"From users\u2019 intentions to if-then rules in the internet of things","volume":"39","author":"Corno","year":"2021","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102869","DOI":"10.1016\/j.ipm.2022.102869","article-title":"What IoT devices and applications should be connected? Predicting user behaviors of IoT services with node2vec embedding","volume":"59","author":"Kim","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huang, Z., Li, J., Zhang, H., Zhang, C., and Yu, G. (2023). TAP-AHGNN: An Attention-Based Heterogeneous Graph Neural Network for Service Recommendation on Trigger-Action Programming Platform. Advanced Intelligent Computing Technology and Applications, Proceedings of the International Conference on Intelligent Computing, Zhengzhou, China, 10\u201313 August 2023, Springer.","DOI":"10.1007\/978-981-99-4752-2_12"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yusuf, I.N.B., Jiang, L., and Lo, D. (2022, January 16\u201317). Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning. Proceedings of the 30th IEEE\/ACM International Conference on Program Comprehension, Pittsburgh, PA, USA.","DOI":"10.1145\/3524610.3527922"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yusuf, I.N.B., Jamal, D.B.A., Jiang, L., and Lo, D. (2022, January 14\u201318). Recipegen++: An automated trigger action programs generator. Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Singapore.","DOI":"10.1145\/3540250.3558913"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.future.2021.11.006","article-title":"Smart objects recommendation based on pre-training with attention and the thing\u2013thing relationship in social Internet of things","volume":"129","author":"Zhang","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yao, Y., Kamani, M.M., Cheng, Z., Chen, L., Joe-Wong, C., and Liu, T. (2023, January 9\u201312). FedRule: Federated rule recommendation system with graph neural networks. Proceedings of the 8th ACM\/IEEE Conference on Internet of Things Design and Implementation, San Antonio, TX, USA.","DOI":"10.1145\/3576842.3582328"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yu, H., Hua, J., and Julien, C. (2021, January 15\u201317). Analysis of ifttt recipes to study how humans use internet-of-things (iot) devices. Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, Coimbra, Portugal.","DOI":"10.1145\/3485730.3494115"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fan, J., He, Y., Tang, B., Li, Q., and Sandhu, R. (2021, January 10\u201313). Ruledger: Ensuring execution integrity in trigger-action IoT platforms. Proceedings of the IEEE INFOCOM 2021-IEEE Conference on Computer Communications, Vancouver, BC, Canada.","DOI":"10.1109\/INFOCOM42981.2021.9488687"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3432192","article-title":"Trace2tap: Synthesizing trigger-action programs from traces of behavior","volume":"4","author":"Zhang","year":"2020","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Manandhar, S., Moran, K., Kafle, K., Tang, R., Poshyvanyk, D., and Nadkarni, A. (2020, January 18\u201321). Towards a natural perspective of smart homes for practical security and safety analyses. Proceedings of the 2020 IEEE Symposium on Security and Privacy (SP 2020), San Francisco, CA, USA.","DOI":"10.1109\/SP40000.2020.00062"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Corno, F., De Russis, L., and Monge Roffarello, A. (2020, January 17\u201320). TAPrec: Supporting the composition of trigger-action rules through dynamic recommendations. Proceedings of the 25th International Conference on Intelligent User Interfaces, Cagliari, Italy.","DOI":"10.1145\/3377325.3377499"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wu, Q., Shen, B., and Chen, Y. (2020). Learning to recommend trigger-action rules for end-user development: A knowledge graph based approach. Reuse in Emerging Software Engineering Practices, Proceedings of the International Conference on Software and Software Reuse, Hammamet, Tunisia, 2\u20134 December 2020, Springer.","DOI":"10.1007\/978-3-030-64694-3_12"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108766","DOI":"10.1016\/j.engappai.2024.108766","article-title":"UIFRS-HAN: User interests-aware food recommender system based on the heterogeneous attention network","volume":"135","author":"Forouzandeh","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., and Xie, X. (2021, January 11\u201315). Self-supervised graph learning for recommendation. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual.","DOI":"10.1145\/3404835.3462862"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yu, J., Yin, H., Xia, X., Chen, T., Cui, L., and Nguyen, Q.V.H. (2022, January 11\u201315). Are graph augmentations necessary? Simple graph contrastive learning for recommendation. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain.","DOI":"10.1145\/3477495.3531937"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Huang, C., and Huang, L. (2023, January 6\u201310). Adaptive graph contrastive learning for recommendation. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, USA.","DOI":"10.1145\/3580305.3599768"},{"key":"ref_19","unstructured":"Jing, M., Zhu, Y., Zang, T., Yu, J., and Tang, F. (2022). Graph contrastive learning with adaptive augmentation for recommendation. Machine Learning and Knowledge Discovery in Databases, Proceedings of the ECML PKDD 2022, Grenoble, France, 19\u201323 September 2022, Springer."},{"key":"ref_20","unstructured":"Jang, E., Gu, S., and Poole, B. (2016). Categorical reparameterization with gumbel-softmax. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, Y., Xie, R., Zhu, Y., Ao, X., Chen, X., Zhang, X., Zhuang, F., Lin, L., and He, Q. (2022). Multi-view multi-behavior contrastive learning in recommendation. Database Systems for Advanced Applications, Proceedings of the DASFAA 2022, Virtual Event, 11\u201314 April 2022, Springer.","DOI":"10.1007\/978-3-031-00126-0_11"},{"key":"ref_22","unstructured":"Oord, A.v.d., Li, Y., and Vinyals, O. (2018). Representation learning with contrastive predictive coding. arXiv."},{"key":"ref_23","unstructured":"Pei, H., Wei, B., Chang, K.C.C., Lei, Y., and Yang, B. (2020). Geom-gcn: Geometric graph convolutional networks. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, Y., Huang, C., Xia, L., and Li, C. (2022, January 11\u201315). Knowledge graph contrastive learning for recommendation. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain.","DOI":"10.1145\/3477495.3532009"},{"key":"ref_25","unstructured":"Kamani, M.M., Yao, Y., Lyu, H., Cheng, Z., Chen, L., Li, L., Joe-Wong, C., and Luo, J. (2024). WYZE rule: Federated rule dataset for rule recommendation benchmarking. Adv. Neural Inf. Process. Syst., 36."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., and Wang, M. (2020, January 25\u201330). Lightgcn: Simplifying and powering graph convolution network for recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event.","DOI":"10.1145\/3397271.3401063"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, L., Wu, L., Hong, R., Zhang, K., and Wang, M. (2020, January 7\u201312). Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Wang, M., Feng, F., and Chua, T.S. (2019, January 21\u201325). Neural graph collaborative filtering. Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval, Paris, France.","DOI":"10.1145\/3331184.3331267"},{"key":"ref_29","unstructured":"Cai, X., Huang, C., Xia, L., and Ren, X. (2023, January 1\u20135). LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. Proceedings of the Eleventh International Conference on Learning Representations, Kigali, Rwanda."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6151\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:00:47Z","timestamp":1760112047000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6151"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,23]]},"references-count":29,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24186151"],"URL":"https:\/\/doi.org\/10.3390\/s24186151","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,23]]}}}