Search (6 results, page 1 of 1)

  • × author_ss:"Zhang, X."
  1. Liu, J.; Zhang, X.: ¬The role of domain knowledge in document selection from search results (2019) 0.01 
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    Abstract
    It is a frequently seen scenario that when people are not familiar with their search topics, they use a simple keyword search, which leads to a large amount of search results in multiple pages. This makes it difficult for users to pick relevant documents, especially given that they are not knowledgeable of the topics. To explore how systems can better help users find relevant documents from search results, the current research analyzed document selection behaviors of users with different levels of domain knowledge (DK). Data were collected in a laboratory study with 35 participants each searching on four tasks in the genomics domain. The results show that users with high and low DK levels selected different sets of documents to view; those high in DK read more documents and gave higher relevance ratings for the viewed documents than those low in DK did. Users with low DK tended to select documents ranking toward the top of the search result lists, and those with high in DK tended to also select documents ranking down the search result lists. The findings help design search systems that can personalize search results to users with different levels of DK.
  2. Zhang, X.; Liu, J.; Cole, M.; Belkin, N.: Predicting users' domain knowledge in information retrieval using multiple regression analysis of search behaviors (2015) 0.01 
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    Abstract
    User domain knowledge affects search behaviors and search success. Predicting a user's knowledge level from implicit evidence such as search behaviors could allow an adaptive information retrieval system to better personalize its interaction with users. This study examines whether user domain knowledge can be predicted from search behaviors by applying a regression modeling analysis method. We identify behavioral features that contribute most to a successful prediction model. A user experiment was conducted with 40 participants searching on task topics in the domain of genomics. Participant domain knowledge level was assessed based on the users' familiarity with and expertise in the search topics and their knowledge of MeSH (Medical Subject Headings) terms in the categories that corresponded to the search topics. Users' search behaviors were captured by logging software, which includes querying behaviors, document selection behaviors, and general task interaction behaviors. Multiple regression analysis was run on the behavioral data using different variable selection methods. Four successful predictive models were identified, each involving a slightly different set of behavioral variables. The models were compared for the best on model fit, significance of the model, and contributions of individual predictors in each model. Each model was validated using the split sampling method. The final model highlights three behavioral variables as domain knowledge level predictors: the number of documents saved, the average query length, and the average ranking position of the documents opened. The results are discussed, study limitations are addressed, and future research directions are suggested.
  3. Zhang, X.; Chignell, M.: Assessment of the effects of user characteristics on mental models of information retrieval systems (2001) 0.00 
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    Date
    29. 9.2001 14:00:33
  4. Zhang, X.; Wang, D.; Tang, Y.; Xiao, Q.: How question type influences knowledge withholding in social Q&A community (2023) 0.00 
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    Date
    22. 9.2023 13:51:47
  5. Jiang, Y.; Bai, W.; Zhang, X.; Hu, J.: Wikipedia-based information content and semantic similarity computation (2017) 0.00 
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    Date
    23. 1.2017 14:06:29
  6. Yang, F.; Zhang, X.: Focal fields in literature on the information divide : the USA, China, UK and India (2020) 0.00 
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    Date
    13. 2.2020 18:22:13

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