Search (7 results, page 1 of 1)

  • × author_ss:"Jansen, B.J."
  • × year_i:[2010 TO 2020}
  1. Jansen, B.J.; Rieh, S.Y.: ¬The seventeen theoretical constructs of information searching and information retrieval (2010) 0.01 
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    Abstract
    In this article, we identify, compare, and contrast theoretical constructs for the fields of information searching and information retrieval to emphasize the uniqueness of and synergy between the fields. Theoretical constructs are the foundational elements that underpin a field's core theories, models, assumptions, methodologies, and evaluation metrics. We provide a framework to compare and contrast the theoretical constructs in the fields of information searching and information retrieval using intellectual perspective and theoretical orientation. The intellectual perspectives are information searching, information retrieval, and cross-cutting; and the theoretical orientations are information, people, and technology. Using this framework, we identify 17 significant constructs in these fields contrasting the differences and comparing the similarities. We discuss the impact of the interplay among these constructs for moving research forward within both fields. Although there is tension between the fields due to contradictory constructs, an examination shows a trend toward convergence. We discuss the implications for future research within the information searching and information retrieval fields.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1517-1534
  2. Liu, Z.; Jansen, B.J.: ASK: A taxonomy of accuracy, social, and knowledge information seeking posts in social question and answering (2017) 0.00 
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    Abstract
    Many people turn to their social networks to find information through the practice of question and answering. We believe it is necessary to use different answering strategies based on the type of questions to accommodate the different information needs. In this research, we propose the ASK taxonomy that categorizes questions posted on social networking sites into three types according to the nature of the questioner's inquiry of accuracy, social, or knowledge. To automatically decide which answering strategy to use, we develop a predictive model based on ASK question types using question features from the perspectives of lexical, topical, contextual, and syntactic as well as answer features. By applying the classifier on an annotated data set, we present a comprehensive analysis to compare questions in terms of their word usage, topical interests, temporal and spatial restrictions, syntactic structure, and response characteristics. Our research results show that the three types of questions exhibited different characteristics in the way they are asked. Our automatic classification algorithm achieves an 83% correct labeling result, showing the value of the ASK taxonomy for the design of social question and answering systems.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.2, S.333-347
  3. Ortiz-Cordova, A.; Jansen, B.J.: Classifying web search queries to identify high revenue generating customers (2012) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1426-1441
  4. Jansen, B.J.; Liu, Z.; Simon, Z.: ¬The effect of ad rank on the performance of keyword advertising campaigns (2013) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.10, S.2115-2132
  5. Ortiz-Cordova, A.; Yang, Y.; Jansen, B.J.: External to internal search : associating searching on search engines with searching on sites (2015) 0.00 
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    Source
    Information processing and management. 51(2015) no.5, S.718-736
  6. Coughlin, D.M.; Campbell, M.C.; Jansen, B.J.: ¬A web analytics approach for appraising electronic resources in academic libraries (2016) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.3, S.518-534
  7. Coughlin, D.M.; Jansen, B.J.: Modeling journal bibliometrics to predict downloads and inform purchase decisions at university research libraries (2016) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.9, S.2263-2273

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