Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (6): 2182-2189.doi: 10.13229/j.cnki.jdxbgxb20210848

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Text fusion based internet of things service recommendation for trigger⁃action programming pattern

Dong-ming SUN(),Liang HU,Yong-heng XING,Feng WANG()   

  1. College of Computer Science and Technology,Jilin University,Changchun 130012,China
  • Received:2021-03-30 Online:2021-11-01 Published:2021-11-15
  • Contact: Feng WANG E-mail:sundm16@mails.jlu.edu.cn;wangfeng12@mails.jlu.edu.cn

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

CLC Number: 

  • TP391.1

Fig.1

TAP structure"

Fig.2

Add "Word" type nodes to the IoT-HIN"

Fig.3

Add "WR" type edges to the IoT-HIN"

Fig.4

Structure of neural network model"

Table 1

Service providers(Channel) list"

IFTTTTwitterDate Time
RSS FeedPhone Call(US only)Weather Underground
EmailSMSDelicious
FacebookClassifiedsTumblr
VimeoFlickrStocks
PinboardPocketEvernote
DropboxInstagramFacebook Pages
WordPressFoursquareYouTube
GmailGoogle CalendarDiigo
SoundCloudBitlyOneDrive
BoxGoogle DriveWithings
LinkedIn

Table 2

Trigger-Channel forecast results"

方法AccuracyPrecisionRecallF1-score
1NN0.6260.6600.6260.619
3NN0.5580.6300.5580.556
NB0.4840.5160.4840.489
DT0.6890.7170.6890.691
SVM0.3050.0930.3050.143
本文0.7940.8010.7940.787

Table 3

Action-Channel forecast results"

方法AccuracyPrecisionRecallF1-score
1NN0.5790.6470.5790.588
3NN0.5210.6340.5210.522
NB0.4320.4720.4320.428
DT0.7210.7360.7210.719
SVM0.1470.0220.1470.038
本文0.7820.7920.7820.779

Table 4

Overall forecast results"

方法AccuracyPrecisionRecallF1-score
1NN0.6030.6430.6030.604
3NN0.5400.6460.5390.546
NB0.4580.4860.4580.461
DT0.7050.7190.7050.704
SVM0.2260.0640.2260.100
本文0.7880.7990.7880.783
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