Software Technology Research
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
533-539

On-device deep learning for smart home service recommendation framework

Chen Jiawen1,2,3
Huang Zhiming1,2,3
Cai Zezhuo4
Chen Xing1,2,3
1. College of Computer & Data Science, Fuzhou University, Fuzhou 350116, China
2. Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou 350002, China
3. Fujian Key Laboratory of Network Computing & Intelligent Information Processing(Fuzhou University), Fuzhou 350116, China
4. School of Computer Science, Beijing Institute of Technology, Beijing 100081, China
DOI: 10.19734/j.issn.1001-3695.2023.06.0262

Abstract

As smart homes become more prevalent, users expect to control smart devices through natural language commands and desire personalized smart home services. However, existing challenges include the interoperability of smart devices and a comprehensive understanding of the user environment. To address these issues, this paper proposed a framework supporting personalized smart home service recommendations for device-end users. Firstly, it constructed a runtime knowledge graph to reflect contextual information in specific smart homes and generated scenario-based sentences. Secondly, it trained a general recommendation model using pre-collected natural language instructions and corresponding scenario-based sentence representations from users in common scenarios. Finally, users interacted with smart home devices and services through natural language on the device end while fine-tuning the weights of the general model through feedback to obtain a personal model. Experimental results on three datasets-basic instruction set, paraphrase set, and scenario instruction set show that the personal model achieves an accuracy improvement of 6.5% to 30% compared to word embedding methods and 2.4% to 25% compared to the Sentence-BERT model, which validates that the device-end deep learning-based smart home service framework has a high ser-vice recommendation accuracy and effectively manages smart home devices and services.

Foundation Support

国家自然科学基金资助项目(62072108)
福建省自然科学基金杰青资助项目(2020J06014)
福建省财政厅科研专项经费资助项目(83021094)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0262
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Software Technology Research
Pages: 533-539
Serial Number: 1001-3695(2024)02-032-0533-07

Publish History

[2023-08-22] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

陈佳雯, 黄志明, 蔡泽卓, 等. 设备端基于深度学习的智能家居服务推荐框架 [J]. 计算机应用研究, 2024, 41 (2): 533-539. (Chen Jiawen, Huang Zhiming, Cai Zezhuo, et al. On-device deep learning for smart home service recommendation framework [J]. Application Research of Computers, 2024, 41 (2): 533-539. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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