ISCAS OpenIR
Diversifying Tag Selection Result for Tag Clouds by Enhancing both Coverage and Dissimilarity
Wang, Meiling; Zhou, Xiang; Tao, Qiuming; wu, Wei; Zhao, Chen
2013
Conference Name14th International Conference on Web Information Systems Engineering (WISE)
Pages29-42
Conference DateOCT 13-15, 2013
Conference PlaceNanjing, PEOPLES R CHINA
Indexed TypeCPCI
Publish PlaceSPRINGER-VERLAG BERLIN
ISSN0302-9743
ISBN978-3-642-41154-0; 978-3-642-41153-3
Department[Wang, Meiling; Zhou, Xiang; Tao, Qiuming; wu, Wei; Zhao, Chen] Chinese Acad Sci, Inst Software, Beijing, Peoples R China.
English AbstractTag cloud has been a popular facility used by social sites for online resource summarization and navigation. Tag selection, which aims to select a limited number of representative tags from a large set of tags, is the core task for creating tag clouds. Diversity of tag selection result is an important factor that affects user satisfaction. Information coverage and item dissimilarity are two major perspectives for exploring the concept of diversity, while existing tag selection approaches usually consider diversification from single perspective. In this paper, we propose a new approach for diversifying tag selection result, which takes into account both information coverage and tag dissimilarity. We design two sub-objective functions about information coverage and tag dissimilarity, respectively, and construct an objective function as a convex combination of the two sub-objective ones. We also give out a greedy algorithm that can well approximate the objective function. We conduct experiments on 17 datasets extracted from the website of CiteULike to compare our approach with existing ones. The experiment results show that our approach can achieve promising performance of diversification.; Tag cloud has been a popular facility used by social sites for online resource summarization and navigation. Tag selection, which aims to select a limited number of representative tags from a large set of tags, is the core task for creating tag clouds. Diversity of tag selection result is an important factor that affects user satisfaction. Information coverage and item dissimilarity are two major perspectives for exploring the concept of diversity, while existing tag selection approaches usually consider diversification from single perspective. In this paper, we propose a new approach for diversifying tag selection result, which takes into account both information coverage and tag dissimilarity. We design two sub-objective functions about information coverage and tag dissimilarity, respectively, and construct an objective function as a convex combination of the two sub-objective ones. We also give out a greedy algorithm that can well approximate the objective function. We conduct experiments on 17 datasets extracted from the website of CiteULike to compare our approach with existing ones. The experiment results show that our approach can achieve promising performance of diversification.
KeywordTag Cloud Tag Selection Result Diversification Coverage Dissimilarity Submodularity Greedy Algorithm
Language英语
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/16528
Collection中国科学院软件研究所
Recommended Citation
GB/T 7714
Wang, Meiling,Zhou, Xiang,Tao, Qiuming,et al. Diversifying Tag Selection Result for Tag Clouds by Enhancing both Coverage and Dissimilarity[C]. SPRINGER-VERLAG BERLIN,2013:29-42.
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