• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.439-467
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• 2021

While the body of cases on startup has grown substantially, there has been a lack of a one-stop gateway of entrepreneurship. The study attempts to build a subject gateway for startup information services based on case studies, users' needs analysis, and literature reviews. The results show that users have difficulty in selecting useful information since the excess of information and the search for the desired information as it is scattered across a wide range of sources. The study designed a subject gateway by a navigation system that enables flexible browsing within the entire gateway through the ontology modeling. The study also presented an example of startup records to display how startup information can be explored. This study is expected to contribute to the understanding of the current status related to business startup services. The business startup digital gateways based on empirical data analysis will contribute to extending library service for startup.

• International Journal of Knowledge Content Development & Technology
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• v.11 no.1
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• pp.85-111
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• 2021

This study aims to analyze users' assessment of electronic libraries in the mobile app market and promote service improvement based on this. To this end, the basic background and purpose of the research, research method, and research scope were first set, and the relevant literature and empirical prior studies were analyzed. Next, users' evaluations of electronic libraries were collected and analyzed from Google Play Store. Based on the results analyzed, measures to improve the quality of electronic libraries were discussed. Based on the results of the study, the following improvement measures are proposed. Need for systemic improvement and stabilization. Provision of applications suitable for multi-device environments. Resumption of services after systematic inspection after updating. Simplification of sign up, log in, and authentication procedures. User support through real-time chat. Introduction of a detailed assessment of reviews. Provision of guidance and user manual for electronic libraries. Improvements to expand user convenience, and Securing differentiation from other similar services.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.586-598
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• 2021

Nowadays, users' reviews and feedback on e-commerce sites stored in text create a huge source of information for analyzing customers' experience with goods and services provided by a business. In other words, collecting and analyzing this information is necessary to better understand customer needs. In this study, we first collected a corpus with 99,322 customers' comments and opinions in English. From this corpus we chose the best number of topics (K) using Perplexity and Coherence Score measurements as the input parameters for the model. Finally, we conducted an experiment using the latent Dirichlet allocation (LDA) topic model with K coefficients to explore the topic. The model results found hidden topics and keyword sets with high probability that are interesting to users. The application of empirical results from the model will support decision-making to help businesses improve products and services as well as business management and development in the field of hotel services.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.354-380
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• 2022

This study proposed a novel model to investigate influential factors affecting the intention to continue using increasingly popular food delivery apps (FDAs). The proposed theoretical model is developed and validated to extend traditional technology acceptance and adoption theories by identifying several determinant factors that capture the unique context of FDAs continuous usage. Hypotheses were tested using a partial least square structural equation modeling approach (PLS-SEM) on data collected from 331 actual FDAs users during the COVID-19 pandemic. The results reveal that convenience, perceived compatibility, delivery experience, and online reviews significantly influence the continuous usage of FDAs. The findings also confirm the importance of continuous intention on the actual use of FDAs. The research model of this study explains 65% of variance in continuous intention and 47% in actual use. The insights provided by this study suggest fruitful directions for future research. They can also help FDAs companies, developers and marketers with strategies and tips for further development and growth by ensuring users' continuous usage of these platforms.

• Asia pacific journal of information systems
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• v.34 no.2
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• pp.620-634
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• 2024

Recently, active research has been conducted on the metaverse as a new education platform. However, only a few studies analyze the specific characteristics of this platform from potential users' perspectives. Therefore, based on literature reviews and expert surveys on education, this study specifies the attributes and levels to be considered in developing metaverse education platforms. An online survey was conducted among university students in South Korea, and conjoint analysis was performed to propose the conditions for education platforms optimized for university education. The results revealed that 85% of respondents were willing to use metaverse education platforms, and preferred virtual classrooms that enable indirect experience in a web-based personal computer environment. In particular, the respondents showed a high preference for the education platforms that were available at $5 per month and used newly created three-dimensional avatar characters of themselves. This study is significant since its results have strategic implications for expanding the metaverse's use as a new educational space.

• Journal of the Korean Society for information Management
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• v.34 no.3
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• pp.269-290
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• 2017

This study aims to present the framework for promoting active engagement of users in digital archives through social tagging. It analyzed the technological development involved with digital archives, and the user participation and engagement through social media. The analysis explored the aspects of social tagging in terms of communication, sharing and collaboration in digital archives. Based on the analysis and reviews, it developed the model of social tagging for user participation and interaction in digital archives. The study proposed the application of open and game platforms for promoting active engagement of users in digital archives through social tagging.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3810-3836
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• 2014

Geo-location white space spectrum databases (GL-WSDBs) are currently the preferred technique for enabling spectrum sharing between primary users and secondary users or white space devices (WSDs) in the very-high frequency (VHF) and ultra-high frequency (UHF) bands. This is true because technologies for making low-cost WSDs capable of autonomous sensing and detection of available white space (WS) spectrum are not yet feasible. This paper reviews the necessary enabling technical conditions to allow coexistence of primary and secondary systems in the VHF and UHF spectrum through a GL-WSDB approach. The practical implementation of South Africa's first GL-WSDB was performed. Results of WS channels available from five cities in South Africa calculated from the implemented GL-WSDB was compared with a commercially available GL-WSDB and was found to be 68% similar. Additionally, results from the implemented GL-WSDB were compared with measurements obtained from field spectrum scanning campaigns at two different locations in Cape Town, South Africa, and was found to be 64% similar.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.103-109
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• 2018

In this paper, we propose a dynamic document classification method which breaks away from existing document classification method with artificial categorization rules focusing on suppliers and has changing categorization rules according to users' needs or social trends. The core of this dynamic document classification method lies in the fact that it creates classification criteria real-time by using topic modeling techniques without standardized category rules, which does not force users to use unnecessary frames. In addition, it can also search the details through the relevance analysis by calculating the relationship between the words that is difficult to grasp by word frequency alone. Rather than for logical and systematic documents, this method proposed can be used more effectively for situation analysis and retrieving information of unstructured data which do not fit the category of existing classification such as VOC (Voice Of Customer), SNS and customer reviews of Internet shopping malls and it can react to users' needs flexibly. In addition, it has no process of selecting the classification rules by the suppliers and in case there is a misclassification, it requires no manual work, which reduces unnecessary workload.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.33-39
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• 2020

The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users' previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users' ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.