设备端基于深度学习的智能家居服务推荐框架.

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 service recommendation accuracy and effectively manages smart home devices and services. [ABSTRACT FROM AUTHOR]