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TAG RECOMMENDATION BASED ON TAG-TOPIC MODEL

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成果类型:
期刊论文、会议论文
作者:
Hu, Rong*;He, Tingting(何婷婷);Li, Fang;Hu, Po
通讯作者:
Hu, Rong
作者机构:
[He, Tingting; Hu, Po; Hu, Rong] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
[Li, Fang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构:
[Hu, Rong] C
Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Tag recommendation;Tag-topic model
期刊:
2012 IEEE 2nd International Conference on Cloud Computing and Intelligent Systems (CCIS) Vols 1-3
ISSN:
2376-5933
年:
2012
卷:
03
页码:
1501-1505
会议名称:
2nd IEEE International Conference on Cloud Computing and Intelligent Systems (CCIS)
会议论文集名称:
International Conference on Cloud Computing and Intelligence Systems
会议时间:
OCT 30-NOV 01, 2012
会议地点:
Hangzhou, PEOPLES R CHINA
会议主办单位:
[Hu, Rong;He, Tingting;Hu, Po] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.^[Li, Fang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议赞助商:
IEEE, IEEE Beijing Sect, Chinese Assoc Artificial Intelligence, Multilingual Europe Technol Alliance, Beijing Univ Posts & Telecommunicat, Tsinghua Univ, Inst Engn & Technol
主编:
Li, D Yang, F Ren, F Wang, W
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-1-4673-1855-6; 978-1-4673-1857-0
基金类别:
NSF of ChinaNational Natural Science Foundation of China (NSFC) [90920005, 61003192]; Major Project of State Language Commission in the Twelfth Five-year Plan Period [ZDI125-1]; Project in the National Science & Technology Pillar Program in the Twelfth Five-year Plan Period [2012BAK24B01]; Program of Introducing Talents of Discipline to UniversitiesMinistry of Education, China - 111 Project [B07042]; NSF of Hubei Province [2011CDA034]; self-determined research funds of CCNU from the colleges' basic research and operation of MOE [CCNU10A02009, CCNU10C01005]
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
国家数字化学习工程技术研究中心
摘要:
With the rapid increase of the social websites, most social tagging systems are allowing users to share and to label various kinds of resources with their favorite tags. However, the uncontrolled use of tags makes the resources attached with some irrelevant even noise tags. To solve the problem, this paper proposes a tag-topic model based approach to recommend tags for resources, which elicits latent topics from resources and maps new resources to these latent topics so as to recommend the most appropriate tags for the resources. The...

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