Search (2 results, page 1 of 1)

  • × author_ss:"Belkin, N."
  • × theme_ss:"Informationsdienstleistungen"
  1. Lin, S.-j.; Belkin, N.: Validation of a model of information seeking over multiple search sessions (2005) 0.01 
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    Abstract
    Most information systems share a common assumption: information seeking is discrete. Such an assumption neither reflects real-life information seeking processes nor conforms to the perspective of phenomenology, "life is a journey constituted by continuous acquisition of knowledge." Thus, this study develops and validates a theoretical model that explains successive search experience for essentially the same information problem. The proposed model is called Multiple Information Seeking Episodes (MISE), which consists of four dimensions: problematic situation, information problem, information seeking process, episodes. Eight modes of multiple information seeking episodes are identified and specified with properties of the four dimensions of MISE. The results partially validate MISE by finding that the original MISE model is highly accurate, but less sufficient in characterizing successive searches; all factors in the MISE model are empirically confirmed, but new factors are identified as weIl. The revised MISE model is shifted from the user-centered to the interaction-centered perspective, taking into account factors of searcher, system, search activity, search context, information attainment, and information use activities.
    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.4, S.393-415
  2. Zhang, X.; Liu, J.; Cole, M.; Belkin, N.: Predicting users' domain knowledge in information retrieval using multiple regression analysis of search behaviors (2015) 0.00 
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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.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.5, S.980-1000

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