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: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-04-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/280892 |
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| _version_ | 1832547872031113216 |
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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. |
| format | Article |
| id | doaj-art-c448d0e7b7a642ff81dae878ecc9f9e0 |
| 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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