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Browsing by Author "Kim, Seonho"

Now showing 1 - 3 of 3
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    Design and Evaluation of Techniques to Utilize Implicit Rating Data in Complex Information Systems.
    Kim, Seonho; Fox, Edward A.; Fan, Weiguo; North, Christopher L.; Tatar, Deborah Gail; Torres, Ricardo da Silva (Department of Computer Science, Virginia Polytechnic Institute & State University, 2007-05-01)
    Research in personalization, including recommender systems, focuses on applications such as in online shopping malls and simple information systems. These systems consider user profile and item information obtained from data explicitly entered by users - where it is possible to classify items involved and to make personalization based on a direct mapping from user or user group to item or item group. However, in complex, dynamic, and professional information systems, such as Digital Libraries, additional capabilities are needed to achieve personalization to support their distinctive features: large numbers of digital objects, dynamic updates, sparse rating data, biased rating data on specific items, and challenges in getting explicit rating data from users. In this report, we present techniques for collecting, storing, processing, and utilizing implicit rating data of Digital Libraries for analysis and decision support. We present our pilot study to find virtual user groups using implicit rating data. We demonstrate the effectiveness of implicit rating data for characterizing users and finding virtual user communities, through statistical hypothesis testing. Further, we describe a visual data mining tool named VUDM (Visual User model Data Mining tool) that utilizes implicit rating data. We provide the results of formative evaluation of VUDM and discuss the problems raised and plans for further studies.
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    Further Development of a Digital Library Curriculum: Evaluation Approaches and New Tools
    Yang, Seungwon; Wildemuth, Barbara M.; Kim, Seonho; Murthy, Uma; Pomerantz, Jeffrey P.; Oh, Sanghee; Fox, Edward A. (2007)
    This presentation is a follow-up to our ICADL 2006 paper and discusses our progress over the past year in developing a digital library curriculum. It presents and describes the current curriculum framework, which now includes ten modules and 41 sub-modules. It provides an overview of the curriculum development lifecycle, and our progress through that lifecycle. In particular, it reports on our evaluation of the modules that have been drafted. It concludes with a description of two new technologies: Superimposed Information (SI) to help resource presentation in a module and Visual User model Data Mining (VUDM) to help long-term module upgrade by visualizing the user community and its trends.
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    Visualizing Users, User Communities, and Usage Trends in Complex Information Systems Using Implicit Rating Data
    Kim, Seonho (Virginia Tech, 2008-04-14)
    Research on personalization, including recommender systems, focuses on applications such as in online shopping malls and simple information systems. These systems consider user profile and item information obtained from data explicitly entered by users. There it is possible to classify items involved and to personalize based on a direct mapping from user or user group to item or item group. However, in complex, dynamic, and professional information systems, such as digital libraries, additional capabilities are needed to achieve personalization to support their distinctive features: large numbers of digital objects, dynamic updates, sparse rating data, biased rating data on specific items, and challenges in getting explicit rating data from users. For this reason, more research on implicit rating data is recommended, because it is easy to obtain, suffers less from terminology issues, is more informative, and contains more user-centered information. In previous reports on my doctoral work, I discussed collecting, storing, processing, and utilizing implicit rating data of digital libraries for analysis and decision support. This dissertation presents a visualization tool, VUDM (Visual User-model Data Mining tool), utilizing implicit rating data, to demonstrate the effectiveness of implicit rating data in characterizing users, user communities, and usage trends of digital libraries. The results of user studies, performed both with typical end-users and with library experts, to test the usefulness of VUDM, support that implicit rating data is useful and can be utilized for digital library analysis software, so that both end users and experts can benefit.