ISCAS OpenIR  > 2010软件所会议论文
tag recommendation based on bayesian principle
Wang Zhonghui; Deng Zhihong
2010
Conference Name6th International Conference on Advanced Data Mining and Applications, ADMA 2010
SourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages191-201
Conference Date40848
Conference PlaceChongqing, China
Indexed Typeei
Publish PlaceGermany
ISSN3029743
ISBN3642173128
Department(1) State Key Lab. of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; (2) Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, China
English AbstractSocial tagging systems have become increasingly a popular way to organize online heterogeneous resources. Tag recommendation is a key feature of social tagging systems. Many works has been done to solve this hard tag recommendation problem and has got same good results these years. Taking into account the complexity of the tagging actions, there still exist many limitations. In this paper, we propose a probabilistic model to solve this tag recommendation problem. The model is based on Bayesian principle, and its very robust and efficient. For evaluating our proposed method, we have conducted experiments on a real dataset extracted from BibSonomy, an online social bookmark and publication sharing system. Our performance study shows that our method achieves good performance when compared with classical approaches. © 2010 Springer-Verlag.
KeywordAlgorithms Bayesian Networks Data Mining Thesauri
SponsorshipNational Natural Science Foundation of China; Chongqing Science and Technology Commission; Chongqing Academy of Science and Technology
Language英语
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/8926
Collection2010软件所会议论文
Recommended Citation
GB/T 7714
Wang Zhonghui,Deng Zhihong. tag recommendation based on bayesian principle[C]. Germany,2010:191-201.
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