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Hicks, D.; Wang, J.: Coverage and overlap of the new social sciences and humanities journal lists (2011)
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- Abstract
- This is a study of coverage and overlap in second-generation social sciences and humanities journal lists, with attention paid to curation and the judgment of scholarliness. We identify four factors underpinning coverage shortfalls: journal language, country, publisher size, and age. Analyzing these factors turns our attention to the process of assessing a journal as scholarly, which is a necessary foundation for every list of scholarly journals. Although scholarliness should be a quality inherent in the journal, coverage falls short because groups assessing scholarliness have different perspectives on the social sciences and humanities literature. That the four factors shape perspectives on the literature points to a deeper problem of fragmentation within the scholarly community. We propose reducing this fragmentation as the best method to reduce coverage shortfalls.
- Date
- 22. 1.2011 13:21:28
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He, R.; Wang, J.; Tian, J.; Chu, C.-T.; Mauney, B.; Perisic, I.: Session analysis of people search within a professional social network (2013)
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- Abstract
- We perform session analysis for our domain of people search within a professional social network. We find that the content-based method is appropriate to serve as a basis for the session identification in our domain. However, there remain some problems reported in previous research which degrade the identification performance (such as accuracy) of the content-based method. Therefore, in this article, we propose two important refinements to address these problems. We describe the underlying rationale of our refinements and then empirically show that the content-based method equipped with our refinements is able to achieve an excellent identification performance in our domain (such as 99.820% accuracy and 99.707% F-measure in our experiments). Next, because the time-based method has extremely low computation costs, which makes it suitable for many real-world applications, we investigate the feasibility of the time-based method in our domain by evaluating its identification performance based on our refined content-based method. Our experiments demonstrate that the performance of the time-based method is potentially acceptable to many real applications in our domain. Finally, we analyze several features of the identified sessions in our domain and compare them with the corresponding ones in general web search. The results illustrate the profession-oriented characteristics of our domain.
- Date
- 19. 4.2013 20:31:22
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Wang, J.; Clements, M.; Yang, J.; Vries, A.P. de; Reinders, M.J.T.: Personalization of tagging systems (2010)
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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.
- Theme
- Social tagging
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Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014)
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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.
-
Shen, R.; Wang, J.; Fox, E.A.: ¬A Lightweight Protocol between Digital Libraries and Visualization Systems (2002)
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- Date
- 22. 2.2003 17:25:39
22. 2.2003 18:15:14
-
Jiang, Z.; Gu, Q.; Yin, Y.; Wang, J.; Chen, D.: GRAW+ : a two-view graph propagation method with word coupling for readability assessment (2019)
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- Date
- 15. 4.2019 13:46:22
-
Wang, J.; Halffman, W.; Zhang, Y.H.: Sorting out journals : the proliferation of journal lists in China (2023)
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- Date
- 22. 9.2023 16:39:23
