Search (19 results, page 1 of 1)

  • × language_ss:"e"
  • × theme_ss:"Social tagging"
  • × year_i:[2010 TO 2020}
  1. Spiteri, L.F.: Incorporating facets into social tagging applications : an analysis of current trends (2010) 0.00 
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    Source
    Cataloging and classification quarterly. 48(2010) no.1, S.94-109
    Year
    2010
  2. Ding, Y.; Jacob, E.K.; Fried, M.; Toma, I.; Yan, E.; Foo, S.; Milojevicacute, S.: Upper tag ontology for integrating social tagging data (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.505-521
    Year
    2010
  3. Antin, J.; Earp, M.: With a little help from my friends : self-interested and prosocial behavior on MySpace Music (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.952-963
    Year
    2010
  4. Wang, J.; Clements, M.; Yang, J.; Vries, A.P. de; Reinders, M.J.T.: Personalization of tagging systems (2010) 0.00 
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    Source
    Information processing and management. 46(2010) no.1, S.58-70
    Year
    2010
  5. Nov, O.; Naaman, M.; Ye, C.: Analysis of participation in an online photo-sharing community : a multidimensional perspective (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.555-566
    Year
    2010
  6. Huang, C.; Fu, T.; Chen, H.: Text-based video content classification for online video-sharing sites (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.891-906
    Year
    2010
  7. Bar-Ilan, J.; Zhitomirsky-Geffet, M.; Miller, Y.; Shoham, S.: ¬The effects of background information and social interaction on image tagging (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.940-951
    Year
    2010
  8. Yi, K.: ¬A semantic similarity approach to predicting Library of Congress subject headings for social tags (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1658-1672
    Year
    2010
  9. Kipp, M.E.I.; Campbell, D.G.: Searching with tags : do tags help users find things? (2010) 0.00 
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    Source
    Knowledge organization. 37(2010) no.4, S.239-255
    Year
    2010
  10. Rorissa, A.: ¬A comparative study of Flickr tags and index terms in a general image collection (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2230-2242
    Year
    2010
  11. Stvilia, B.; Jörgensen, C.: Member activities and quality of tags in a collection of historical photographs in Flickr (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.12, S.2477-2489
    Year
    2010
  12. Matthews, B.; Jones, C.; Puzon, B.; Moon, J.; Tudhope, D.; Golub, K.; Nielsen, M.L.: ¬An evaluation of enhancing social tagging with a knowledge organization system (2010) 0.00 
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    Source
    Aslib proceedings. 62(2010) nos.4/5, S.447-465
    Year
    2010
  13. Tsui, E.; Wang, W.M.; Cheung, C.F.; Lau, A.S.M.: ¬A concept-relationship acquisition and inference approach for hierarchical taxonomy construction from tags (2010) 0.00 
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    Information processing and management. 46(2010) no.1, S.44-57
    Year
    2010
  14. Naderi, H.; Rumpler, B.: PERCIRS: a system to combine personalized and collaborative information retrieval (2010) 0.00 
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    Source
    Journal of documentation. 66(2010) no.4, S.532-562
    Year
    2010
  15. Wei, W.; Ram, S.: Utilizing sozial bookmarking tag space for Web content discovery : a social network analysis approach (2010) 0.00 
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    2010
  16. Hsu, M.-H.; Chen, H.-H.: Efficient and effective prediction of social tags to enhance Web search (2011) 0.00 
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    Abstract
    As the web has grown into an integral part of daily life, social annotation has become a popular manner for web users to manage resources. This method of management has many potential applications, but it is limited in applicability by the cold-start problem, especially for new resources on the web. In this article, we study automatic tag prediction for web pages comprehensively and utilize the predicted tags to improve search performance. First, we explore the stabilizing phenomenon of tag usage in a social bookmarking system. Then, we propose a two-stage tag prediction approach, which is efficient and is effective in making use of early annotations from users. In the first stage, content-based ranking, candidate tags are selected and ranked to generate an initial tag list. In the second stage, random-walk re-ranking, we adopt a random-walk model that utilizes tag co-occurrence information to re-rank the initial list. The experimental results show that our algorithm effectively proposes appropriate tags for target web pages. In addition, we present a framework to incorporate tag prediction in a general web search. The experimental results of the web search validate the hypothesis that the proposed framework significantly enhances the typical retrieval model.
  17. Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.00 
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    Date
    25.12.2012 15:22:37
  18. Choi, Y.; Syn, S.Y.: Characteristics of tagging behavior in digitized humanities online collections (2016) 0.00 
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    Date
    21. 4.2016 11:23:22
  19. Qin, C.; Liu, Y.; Mou, J.; Chen, J.: User adoption of a hybrid social tagging approach in an online knowledge community (2019) 0.00 
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    Date
    20. 1.2015 18:30:22

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