Search (10 results, page 1 of 1)

  • × author_ss:"Wang, J."
  1. Hicks, D.; Wang, J.: Coverage and overlap of the new social sciences and humanities journal lists (2011) 0.01
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    Date
    22. 1.2011 13:21:28
  2. Wang, J.; Clements, M.; Yang, J.; Vries, A.P. de; Reinders, M.J.T.: Personalization of tagging systems (2010) 0.01
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    Abstract
    Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user's tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user's own preference and the opinion of others.
  3. Shen, R.; Wang, J.; Fox, E.A.: ¬A Lightweight Protocol between Digital Libraries and Visualization Systems (2002) 0.01
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:15:14
  4. Wang, J.; Guan, J.: ¬The analysis and evaluation of knowledge efficiency in research groups (2005) 0.00
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    Date
    22.10.2005 18:42:28
  5. Oard, D.W.; He, D.; Wang, J.: User-assisted query translation for interactive cross-language information retrieval (2008) 0.00
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    Date
    28. 7.2008 15:08:49
  6. Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014) 0.00
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    Abstract
    In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three benchmark datasets (IAPR TC-12 [8], Flickr 8K [28], and Flickr 30K [13]). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
  7. Thomas, I.S.; Wang, J.; GPT-3: Was euch zu Menschen macht : Antworten einer künstlichen Intelligenz auf die großen Fragen des Lebens (2022) 0.00
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    Date
    7. 1.2023 18:41:29
  8. He, R.; Wang, J.; Tian, J.; Chu, C.-T.; Mauney, B.; Perisic, I.: Session analysis of people search within a professional social network (2013) 0.00
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    Date
    19. 4.2013 20:31:22
  9. Jiang, Z.; Gu, Q.; Yin, Y.; Wang, J.; Chen, D.: GRAW+ : a two-view graph propagation method with word coupling for readability assessment (2019) 0.00
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    Date
    15. 4.2019 13:46:22
  10. Wang, J.; Halffman, W.; Zhang, Y.H.: Sorting out journals : the proliferation of journal lists in China (2023) 0.00
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    Date
    22. 9.2023 16:39:23