Constructing Topic Models of Internet of Things for Information Processing
Internet of Things (IoT) is regarded as a remarkable development of the modern information technology. There is abundant digital products data on the IoT, linking with multiple types of objects/entities. Those associated entities carry rich information and usually in the form of query records. There...
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doaj-b392edccbf034488ba0e2b55b4b31c3f2020-11-25T02:19:12ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/675234675234Constructing Topic Models of Internet of Things for Information ProcessingJie Xin0Zhiming Cui1Shukui Zhang2Tianxu He3Chunhua Li4Haojing Huang5The Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou 215006, ChinaInternet of Things (IoT) is regarded as a remarkable development of the modern information technology. There is abundant digital products data on the IoT, linking with multiple types of objects/entities. Those associated entities carry rich information and usually in the form of query records. Therefore, constructing high quality topic hierarchies that can capture the term distribution of each product record enables us to better understand users’ search intent and benefits tasks such as taxonomy construction, recommendation systems, and other communications solutions for the future IoT. In this paper, we propose a novel record entity topic model (RETM) for IoT environment that is associated with a set of entities and records and a Gibbs sampling-based algorithm is proposed to learn the model. We conduct extensive experiments on real-world datasets and compare our approach with existing methods to demonstrate the advantage of our approach.http://dx.doi.org/10.1155/2014/675234 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jie Xin Zhiming Cui Shukui Zhang Tianxu He Chunhua Li Haojing Huang |
spellingShingle |
Jie Xin Zhiming Cui Shukui Zhang Tianxu He Chunhua Li Haojing Huang Constructing Topic Models of Internet of Things for Information Processing The Scientific World Journal |
author_facet |
Jie Xin Zhiming Cui Shukui Zhang Tianxu He Chunhua Li Haojing Huang |
author_sort |
Jie Xin |
title |
Constructing Topic Models of Internet of Things for Information Processing |
title_short |
Constructing Topic Models of Internet of Things for Information Processing |
title_full |
Constructing Topic Models of Internet of Things for Information Processing |
title_fullStr |
Constructing Topic Models of Internet of Things for Information Processing |
title_full_unstemmed |
Constructing Topic Models of Internet of Things for Information Processing |
title_sort |
constructing topic models of internet of things for information processing |
publisher |
Hindawi Limited |
series |
The Scientific World Journal |
issn |
2356-6140 1537-744X |
publishDate |
2014-01-01 |
description |
Internet of Things (IoT) is regarded as a remarkable development of the modern information technology. There is abundant digital products data on the IoT, linking with multiple types of objects/entities. Those associated entities carry rich information and usually in the form of query records. Therefore, constructing high quality topic hierarchies that can capture the term distribution of each product record enables us to better understand users’ search intent and benefits tasks such as taxonomy construction, recommendation systems, and other communications solutions for the future IoT. In this paper, we propose a novel record entity topic model (RETM) for IoT environment that is associated with a set of entities and records and a Gibbs sampling-based algorithm is proposed to learn the model. We conduct extensive experiments on real-world datasets and compare our approach with existing methods to demonstrate the advantage of our approach. |
url |
http://dx.doi.org/10.1155/2014/675234 |
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1724877612100091904 |