基于文本融合的物联网触发动作编程模式服务推荐方法

doi:10.13229/j.cnki.jdxbgxb20210848

摘要/Abstract

摘要：

针对一类物联网自动化驱动应用提出了一种服务推荐方法。该方法结合了文本分析、异构信息网络、深度学习技术，通过分析文本来寻找物联网中相关联的对象，进而达到推荐服务的目的。首先，构造异构信息网络，并将文本和文本关系融入其中，生成一种文本融合的异构信息网络；随后，设计元路径和关系权重；最终，训练生成神经网络模型用于对服务进行推荐。在一个真实的数据集上进行了实验评估，结果表明，本文方法可以对服务进行推荐，并明显优于一些经典方法。

关键词: 计算机应用, 物联网, 异构信息网络, 文本分析, 触发动作编程模式

Abstract:

This article proposes a service recommendation approach for a type of Internet of Things (IoT) automation-driven applications. The approach combines text analysis, heterogeneous information networks and deep learning to find related objects in the IoT by analyzing text, and then recommends the services. First, we construct a text fusion heterogeneous information network, which integrates text and text relationships. Then we design meta-paths and relationship weights. Finally, we train a neural network model for recommending the services. This paper conducts an experimental evaluation on a real data set. The results show that the proposed approach can be used for recommendation task and outperforms some classical methods.

Key words: computer application, internet of things, heterogeneous information network, text analysis, trigger-action programming pattern

中图分类号:

TP391.1

孙东明,胡亮,邢永恒,王峰. 基于文本融合的物联网触发动作编程模式服务推荐方法[J]. 吉林大学学报(工学版), 2021, 51(6): 2182-2189.

Dong-ming SUN,Liang HU,Yong-heng XING,Feng WANG. Text fusion based internet of things service recommendation for trigger⁃action programming pattern[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2182-2189.

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方法	Accuracy	Precision	Recall	F₁-score
1NN	0.626	0.660	0.626	0.619
3NN	0.558	0.630	0.558	0.556
NB	0.484	0.516	0.484	0.489
DT	0.689	0.717	0.689	0.691
SVM	0.305	0.093	0.305	0.143
本文	0.794	0.801	0.794	0.787

方法	Accuracy	Precision	Recall	F₁-score
1NN	0.579	0.647	0.579	0.588
3NN	0.521	0.634	0.521	0.522
NB	0.432	0.472	0.432	0.428
DT	0.721	0.736	0.721	0.719
SVM	0.147	0.022	0.147	0.038
本文	0.782	0.792	0.782	0.779

方法	Accuracy	Precision	Recall	F₁-score
1NN	0.603	0.643	0.603	0.604
3NN	0.540	0.646	0.539	0.546
NB	0.458	0.486	0.458	0.461
DT	0.705	0.719	0.705	0.704
SVM	0.226	0.064	0.226	0.100
本文	0.788	0.799	0.788	0.783