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  • × author_ss:"Zhang, Y."
  1. Zhang, Y.: Developing a holistic model for digital library evaluation (2010) 0.01 
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    Abstract
    This article reports the author's recent research in developing a holistic model for various levels of digital library (DL) evaluation in which perceived important criteria from heterogeneous stakeholder groups are organized and presented. To develop such a model, the author applied a three-stage research approach: exploration, confirmation, and verification. During the exploration stage, a literature review was conducted followed by an interview, along with a card sorting technique, to collect important criteria perceived by DL experts. Then the criteria identified were used for developing an online survey during the confirmation stage. Survey respondents (431 in total) from 22 countries rated the importance of the criteria. A holistic DL evaluation model was constructed using statistical techniques. Eventually, the verification stage was devised to test the reliability of the model in the context of searching and evaluating an operational DL. The proposed model fills two lacunae in the DL domain: (a) the lack of a comprehensive and flexible framework to guide and benchmark evaluations, and (b) the uncertainty about what divergence exists among heterogeneous DL stakeholders, including general users.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.88-110
    Year
    2010
  2. Zhang, Y.: Dimensions and elements of people's mental models of an information-rich Web space (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2206-2218
    Year
    2010
  3. Zhang, Y.; Zhang, C.; Li, J.: Joint modeling of characters, words, and conversation contexts for microblog keyphrase extraction (2020) 0.00 
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    Abstract
    Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word-level indicative representation and message-level indicative representation hierarchically. In both of the modules, we apply character-level representations, which enables the model to explore morphological features and deal with the out-of-vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real-life data sets indicate that our model outperforms state-of-the-art models from previous studies.
  4. Zhang, Y.; Xu, W.: Fast exact maximum likelihood estimation for mixture of language model (2008) 0.00 
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    Abstract
    Language modeling is an effective and theoretically attractive probabilistic framework for text information retrieval. The basic idea of this approach is to estimate a language model of a given document (or document set), and then do retrieval or classification based on this model. A common language modeling approach assumes the data D is generated from a mixture of several language models. The core problem is to find the maximum likelihood estimation of one language model mixture, given the fixed mixture weights and the other language model mixture. The EM algorithm is usually used to find the solution. In this paper, we proof that an exact maximum likelihood estimation of the unknown mixture component exists and can be calculated using the new algorithm we proposed. We further improve the algorithm and provide an efficient algorithm of O(k) complexity to find the exact solution, where k is the number of words occurring at least once in data D. Furthermore, we proof the probabilities of many words are exactly zeros, and the MLE estimation is implemented as a feature selection technique explicitly.
  5. Zhang, Y.: Complex adaptive filtering user profile using graphical models (2008) 0.00 
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    Abstract
    This article explores how to develop complex data driven user models that go beyond the bag of words model and topical relevance. We propose to learn from rich user specific information and to satisfy complex user criteria under the graphical modelling framework. We carried out a user study with a web based personal news filtering system, and collected extensive user information, including explicit user feedback, implicit user feedback and some contextual information. Experimental results on the data set collected demonstrate that the graphical modelling approach helps us to better understand the complex domain. The results also show that the complex data driven user modelling approach can improve the adaptive information filtering performance. We also discuss some practical issues while learning complex user models, including how to handle data noise and the missing data problem.
  6. Zhang, Y.: ¬The impact of Internet-based electronic resources on formal scholarly communication in the area of library and information science : a citation analysis (1998) 0.00 
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    Date
    30. 1.1999 17:22:22
  7. Zhang, Y.; Jansen, B.J.; Spink, A.: Identification of factors predicting clickthrough in Web searching using neural network analysis (2009) 0.00 
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    Date
    22. 3.2009 17:49:11
  8. Zhang, Y.; Liu, J.; Song, S.: ¬The design and evaluation of a nudge-based interface to facilitate consumers' evaluation of online health information credibility (2023) 0.00 
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    Date
    22. 6.2023 18:18:34
  9. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.00 
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    Date
    22. 6.2023 18:07:12

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