JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2015, Vol. 45 ›› Issue (2): 37-42.doi: 10.6040/j.issn.1672-3961.2.2014.125

Previous Articles     Next Articles

Tag optimization based on semantic similarity

QIAN Suchi, PENG Furong, LU Jianfeng   

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
  • Received:2014-05-23 Revised:2014-11-20 Online:2015-04-20 Published:2014-05-23

Abstract: To effectively solve those problems such as lack and misuse of tags in the social media, a tag optimization method based on content similarity and semantic similarity was proposed. Firstly, TF-IDF(term frequency—inverse document frequency) was used to calculate the text similarity. Afterwards, the objective function was defined by the consistency between text similarity and tag similarity. Finally, correction term was added in optimization process to reduce the deviation of tags provided by users. The objective function was applied to Douban Movie to optimize movie tags and the results were compared and analyzed with the original tags. The accuracy of the optimized tags was improved by comparison. Experimental results showed that the method could effectively optimize tags and solve those problems such as lack and misuse of tags.

Key words: semantic similarity, social media, content similarity, tag optimization, movie tags

CLC Number: 

  • TP391.3
[1] BOYD D M, ELLISON N B. Social network sites:definition, history, and scholarship[J]. Engineering Management Review, IEEE, 2010, 38(3):16-31.
[2] SIGURBJRNSSON B, VAN Zwol R. Flickr tag recommendation based on collective knowledge[C]//Proceedings of the 17th International Conference on World Wide Web. Beijing, China:ACM, 2008:327-336.
[3] 张斌,张引,高克宁,等. 融合关系与内容分析的社会标签推荐[J]. 软件学报, 2012, 23(3):476-488. ZHANG Bin, ZHANG Yin, GAO Kening,et al.Combining relation and content analysis for social tagging recommendation[J]. Journal of Software, 2012, 23(3):476-488.
[4] KRESTEL R, FANKHAUSER P, NEJDL W. Latent dirichlet allocation for tag recommendation[C]//Proceedings of the Third ACM Conference on Recommender Systems. New York, USA:ACM, 2009:61-68.
[5] SYMEONIDIS P, NANOPOULOS A, MANOLOPOULOS Y. Tag recommendations based on tensor dimensionality reduction[C]//Proceedings of the 2008 ACM Conference on Recommender Systems. Lausanne:ACM, 2008:43-50.
[6] JASCHKE R, MARINHO L, HOTHO A, et al. Tag recommendations in folksonomies[M]// Berlin Heidelberg:Springer, 2007:506-514.
[7] LEE SEUNG. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755):788-791.
[8] CARBONELL J, GOLDSTEIN J. The use of MMR, diversity-based reranking for reordering documents and producing summaries[C]//Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA:ACM, 1998:335-336.
[9] 张雷鸣, 李秋丹, 廖胜才. 非负矩阵分解在标签语义分析中的应用[J]. 计算机科学, 2010, 37(4):171-174. ZHANG Leiming, LI Qiudan, LIAO Shengcai. Application of non-negative matrix factorization in tag semantics analysis[J]. Computer Science, 2010, 37(4):171-174.
[10] HOYER P O. Non-negative matrix factorization with sparseness constraints[J]. Journal of Machine Learning Research, 2004(5):1457-1469.
[11] JIANG B. Improving collaborative tag recommendation by using local lexicon in social comment context[C]//Proceedings of 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD). Lausanne, Switzerland:IEEE, 2011:577-580.
[12] LIU D, HUA X S, WANG M, et al. Retagging social images based on visual and semantic consistency[C]//Proceedings of the 19th International Conference on World Wide Web. Raleigh, USA:ACM, 2010:1149-1150.
[13] HOFMANN T. Unsupervised learning by probabilistic latent semantic analysis[J]. Machine Learning, 2001, 42(1-2):177-196.
[14] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. Journal of Machine Learning Research, 2003(3):993-1022.
[15] MERIALDO B. Tagging English text with a probabilistic model[J]. Computational Linguistics, 1994, 20(2):155-171.
[16] 计智伟,胡珉,尹建新. 特征选择算法综述[J]. 电子设计工程,2011,19(9):46-51. JI Zhiwei, HU Min, YIN Jianxin. A survey of feature selection algorithm[J]. Electronic Design Engineering, 2011, 19(9):46-51.
[17] 夏天. 汉语词语语义相似度计算研究[J]. 计算机工程, 2007, 33(6):191-194. XIA Tian. Study on Chinese words semantic similarity computation[J]. Computer Engineering, 2007, 33(6):191-194.
[18] LIN D. Using syntactic dependency as local context to resolve word sense ambiguity[C]//Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics. New York, USA: Association for Computational Linguistics, 1997:64-71.
[19] LIU D, WANG M, YANG Y, et al. Tag quality improvement for social images[C]//Proceedings of IEEE International Conference on Multimedia and Expo. New York, USA:IEEE, 2009:350-353.
[20] LEE D D, SEUNG H S. Algorithms for non-negative matrix factorization[J]. Advances in Neural Information Processing Systems, 2000(3):556-562.
[1] LIN Jianghao, ZHOU Yongmei, YANG Aimin, CHEN Jin. Building of domain sentiment lexicon based on word2vec [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 40-47.
[2] XU Qing, DUAN Liguo, LI Aiping, YIN Guimei. Chinese entity relation extraction based on entity semantic similarity [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(6): 7-15.
[3] YIN Kun, YIN Hongfeng*, YANG Yan, JIA Zhen. Semantic similarity computation of Baidu encyclopedia entries based on SimRank [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(3): 29-35.
Full text



No Suggested Reading articles found!