A Dynamic Users’ Interest Discovery Model with Distributed Inference Algorithm

One of the key issues for providing users user-customized or context-aware services is to automatically detect latent topics, users’ interests, and their changing patterns from large-scale social network information. Most of the current methods are devoted either to discovering static latent topics...

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Main Authors: Shuo Xu, Qingwei Shi, Xiaodong Qiao, Lijun Zhu, Han Zhang, Hanmin Jung, Seungwoo Lee, Sung-Pil Choi
Format: Article
Language:English
Published: Wiley 2014-04-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2014/280892
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author Shuo Xu
Qingwei Shi
Xiaodong Qiao
Lijun Zhu
Han Zhang
Hanmin Jung
Seungwoo Lee
Sung-Pil Choi
author_facet Shuo Xu
Qingwei Shi
Xiaodong Qiao
Lijun Zhu
Han Zhang
Hanmin Jung
Seungwoo Lee
Sung-Pil Choi
author_sort Shuo Xu
collection DOAJ
description One of the key issues for providing users user-customized or context-aware services is to automatically detect latent topics, users’ interests, and their changing patterns from large-scale social network information. Most of the current methods are devoted either to discovering static latent topics and users’ interests or to analyzing topic evolution only from intrafeatures of documents, namely, text content, without considering directly extrafeatures of documents such as authors. Moreover, they are applicable only to the case of single processor. To resolve these problems, we propose a dynamic users’ interest discovery model with distributed inference algorithm, named as Distributed Author-Topic over Time (D-AToT) model. The collapsed Gibbs sampling method following the main idea of MapReduce is also utilized for inferring model parameters. The proposed model can discover latent topics and users’ interests, and mine their changing patterns over time. Extensive experimental results on NIPS (Neural Information Processing Systems) dataset show that our D-AToT model is feasible and efficient.
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institution Kabale University
issn 1550-1477
language English
publishDate 2014-04-01
publisher Wiley
record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-c448d0e7b7a642ff81dae878ecc9f9e02025-02-03T06:43:04ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-04-011010.1155/2014/280892280892A Dynamic Users’ Interest Discovery Model with Distributed Inference AlgorithmShuo Xu0Qingwei Shi1Xiaodong Qiao2Lijun Zhu3Han Zhang4Hanmin Jung5Seungwoo Lee6Sung-Pil Choi7 Information Technology Support Center, Institute of Scientific and Technical Information of China, No. 15 Fuxing Road, Haidian District, Beijing 100038, China School of Software, Liaoning Technical University, No. 188 Longwan Street South, Huludao, Liaoning 125105, China College of Software, Northeast Normal University, 5268 Renmin Street, Changchun, Jilin 130024, China Information Technology Support Center, Institute of Scientific and Technical Information of China, No. 15 Fuxing Road, Haidian District, Beijing 100038, China Information Technology Support Center, Institute of Scientific and Technical Information of China, No. 15 Fuxing Road, Haidian District, Beijing 100038, China Department of Computer Intelligence Research, Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea Department of Computer Intelligence Research, Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea Department of Computer Intelligence Research, Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 305-806, Republic of KoreaOne of the key issues for providing users user-customized or context-aware services is to automatically detect latent topics, users’ interests, and their changing patterns from large-scale social network information. Most of the current methods are devoted either to discovering static latent topics and users’ interests or to analyzing topic evolution only from intrafeatures of documents, namely, text content, without considering directly extrafeatures of documents such as authors. Moreover, they are applicable only to the case of single processor. To resolve these problems, we propose a dynamic users’ interest discovery model with distributed inference algorithm, named as Distributed Author-Topic over Time (D-AToT) model. The collapsed Gibbs sampling method following the main idea of MapReduce is also utilized for inferring model parameters. The proposed model can discover latent topics and users’ interests, and mine their changing patterns over time. Extensive experimental results on NIPS (Neural Information Processing Systems) dataset show that our D-AToT model is feasible and efficient.https://doi.org/10.1155/2014/280892
spellingShingle Shuo Xu
Qingwei Shi
Xiaodong Qiao
Lijun Zhu
Han Zhang
Hanmin Jung
Seungwoo Lee
Sung-Pil Choi
A Dynamic Users’ Interest Discovery Model with Distributed Inference Algorithm
International Journal of Distributed Sensor Networks
title A Dynamic Users’ Interest Discovery Model with Distributed Inference Algorithm
title_full A Dynamic Users’ Interest Discovery Model with Distributed Inference Algorithm
title_fullStr A Dynamic Users’ Interest Discovery Model with Distributed Inference Algorithm
title_full_unstemmed A Dynamic Users’ Interest Discovery Model with Distributed Inference Algorithm
title_short A Dynamic Users’ Interest Discovery Model with Distributed Inference Algorithm
title_sort dynamic users interest discovery model with distributed inference algorithm
url https://doi.org/10.1155/2014/280892
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