Search (3 results, page 1 of 1)

  • × author_ss:"Belkin, N.J."
  • × type_ss:"a"
  • × year_i:[2010 TO 2020}
  1. Yuan, X.; Belkin, N.J.: Evaluating an integrated system supporting multiple information-seeking strategies (2010) 0.02 
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    Abstract
    Many studies have demonstrated that people engage in a variety of different information behaviors when engaging in information seeking. However, standard information retrieval systems such as Web search engines continue to be designed to support mainly one such behavior, specified searching. This situation has led to suggestions that people would be better served by information retrieval systems which support different kinds of information-seeking strategies. This article reports on an experiment comparing the retrieval effectiveness of an integrated interactive information retrieval (IIR) system which adapts to support different information-seeking strategies with that of a standard baseline IIR system. The experiment, with 32 participants each searching on eight different topics, indicates that using the integrated IIR system resulted in significantly better user satisfaction with search results, significantly more effective interaction, and significantly better usability than that using the baseline system.
  2. Liu, J.; Liu, C.; Belkin, N.J.: Predicting information searchers' topic knowledge at different search stages (2016) 0.01 
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    Abstract
    As a significant contextual factor in information search, topic knowledge has been gaining increased research attention. We report on a study of the relationship between information searchers' topic knowledge and their search behaviors, and on an attempt to predict searchers' topic knowledge from their behaviors during the search. Data were collected in a controlled laboratory experiment with 32 undergraduate journalism student participants, each searching on 4 tasks of different types. In general, behavioral variables were not found to have significant differences between users with high and low levels of topic knowledge, except the mean first dwell time on search result pages. Several models were built to predict topic knowledge using behavioral variables calculated at 3 different stages of search episodes: the first-query-round, the middle point of the search, and the end point. It was found that a model using some search behaviors observed in the first query round led to satisfactory prediction results. The results suggest that early-session search behaviors can be used to predict users' topic knowledge levels, allowing personalization of search for users with different levels of topic knowledge, especially in order to assist users with low topic knowledge.
  3. Yuan, X. (J.); Belkin, N.J.: Applying an information-seeking dialogue model in an interactive information retrieval system (2014) 0.01 
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    Date
    6. 4.2015 19:22:59

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