• Journal of the Korean Society for information Management
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• v.30 no.3
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• pp.183-205
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• 2013

A number of studies and models have focused on collaborative information behavior (CIB) in various contexts as the importance of research on CIB is recognized in information behavior research. This study compared and analyzed the models of CIB developed by Prekop (2002), Reddy and Jansen (2008), Shah (2008), Karunakaran, Spence and Reddy (2010), and Yue and He (2010), and discussed the direction of future studies of CIB. The future research strategies for overcoming the limitation of previous studies and models on CIB are to extend research object and context, to do holistic research approach tracing complexity of CIB, and to debate and verify the models of CIB. In particular, it needs to modify and improve the models based on the empirical research for domestic context.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.3
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• pp.97-117
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• 2013

This study aimed to explore group members' collaborative information by analyzing the number and the content of text contributions on the online discussion board in the group-based research project. This study explored graduate students' collaborative information behavior, affective approach, and types of collaboration and support needed in the group-based research project based on Kuhlthau's Information Search Process(ISP) Model and Yue and He's Collaborative Information Behavior(CIB) Model. It is expected that the results of this study will be useful for understanding of CIB in the group-based research project and applying information literacy instruction to information user in collaboration.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.17-31
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• 2023

An activity-based model requires detailed population information to model individual travel behavior in a disaggregated manner. The recent innovative approach developed deep generative models with novel regularization terms that improves fidelity and diversity for population synthesis. Since the method relies on measuring the distance between distribution boundaries of the sample data and the generated sample, it is crucial to obtain well-defined continuous representation from the discretized dataset. Therefore, we propose an improved entity embedding models to enhance the performance of the regularization terms, which indirectly supports the synthesis in terms of feasible and diverse populations. Our results show a 28.87% improvement in the F1 score compared to the baseline method.