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| Tag Clustering Method of Joint Topic Model |
| HU Xuegang1, LI Huizong1,2, PAN Jianhan3, HE Wei1, YANG Hengyu1 |
1.School of Computer and Information, Hefei University of Technology, Hefei 230009
2. School of Economics and Management, Anhui University of Science and Technology, Huainan 232001
3. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116 |
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Abstract Improving the clustering quality of social tags is a key problem in the semantics recognition of tags. A joint topic model based on resource is proposed to cluster tags. Firstly, reference relations of the resource are utilized to acquire the authority scores of resource by using random walk method. Secondly, the resource authority is applied to set the weights of two binary relations of resource-tag and resource word. Grounded on that, the joint latent Dirichlet allocation(LDA) model of the word and the tag based on resource weighted is constructed. By iterative learning, the latent topics of the tag are acquired, and the clusters are decided according to the maximum membership degree of the tag. The results show that the proposed method has a better clustering performance than other tag clustering methods based on resource.
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Received: 05 May 2016
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About author:: (HU Xuegang, born in 1961, Ph.D., professor. His research interests include data mining and information processing.)
(LI Huizong(Corresponding author), born in 1979, Ph.D., associate professor. His research interests include intelligent information processing.)
(PAN Jianhan, born in 1983, Ph.D., lecturer. His research interests include transfer learning.)
(HE Wei, born in 1986, Ph.D. candidate. His research interests include social network.)
(YANG Hengyu, born in 1973, Ph.D. candidate. His research interests include information processing.) |
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2017, Vol. 30 
