• Journal of the Korean Society for information Management
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• v.20 no.2
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• pp.135-156
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• 2003

The purpose of this study was to provide suggestions for the effective management and user services of electronic journals in university libraries. In order to do that literature reviews, case studies, and survey questionnaires were conducted. The case studies involved six university libraries, which were analyzed based on accessing, cataloging and user services. Questionnaires consisting of forty-seven items in eight parts, including selection and subscription of electronic journals were distributed to the libratians who were in charge of managing electronic journals. Following an analysis on the survey, guidelines for effective management and user services of electronic journals were suggested.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.2
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• pp.261-271
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• 2016

The need and interest for a successful operation of Information System in the defense industry have increased due to a rapid development in ICT. However, research on adderssing such increased demand has been minimal. This paper introduces a Success Model for IS in the defense field based on literature reviews, and proves the performance of the proposed model. The verification results show that the quality factors of IS have positive influence on perceived usefulness and user satisfaction. The results, as well, imply that perceived usefulness and user satisfaction improves the operational performance of IS. Therefore, this study proves that quality factors of IS increase the operational performance through perceived usefulness and user satisfaction. Through this research, the patterns of information utilization in the defense IS is understood, and the directions for improving the operational performance of IS is presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.502-503
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• 2019

In the existing system that recommends through review data analysis, it does not reflect personal preference details such as user's characteristics or product purchase tastes, in this paper, we propose a system that provides customized recommendation information to various users by selecting the criterion that the user thinks most importantly when searching for the product and purchasing the product, and analyzing it. This is because the user's personal preference is reflected by arranging the product list based on the criterion that the user occupies the largest portion of the product purchase, so that it is more efficient than the recommendation through the recommendation system.

• Journal of information and communication convergence engineering
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• v.12 no.3
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• pp.161-167
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• 2014

Web caches help to reduce latencies arising from slow networks through storing and reusing what was used before. Repeat access to a cached resource does not incur network latencies. However, resources that have never been used will not be found in the cache. Cache prefetching is a technique that helps to fill a cache with still-unused resources in anticipation that these resources will be used in the near future. Typically these unused resources are related to the resources that have been accessed in the recent past. While web caching exploits temporal locality, prefetching attempts to exploit spatial locality. Access to the prefetched resources will be cache hits, and therefore reduces the latency as perceived by the user. This paper reviews the cache infrastructure supported by the hypertext transfer protocol and discusses web cache prefetching in general, including Mozilla's prefetching infrastructure. It then classifies and reviews some prefetching techniques.

• The Journal of Information Systems
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• v.14 no.2
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• pp.133-154
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• 2005

The objective of this study is to find the factors influencing the use of mobile service. We extracted critical study variables according to literature reviews about adoption of information systems, those under the internet environment and mobile internet adoption for theoretical foundation. TAM suggested by Davis(1985) has explained the acceptance mechanism with the following constructs: perceived usefulness, perceived ease of use, attitude toward use, behavioral intention and actual usage. After studying prior literature reviews, this study investigated the group of people using mobile services. The result of study claims that it has same results with previous TAM, especially between personal innovativeness and attitude toward use have strong statistical relations. On the other hand, there haven't been any statistical relations between perceived ease of use and perceived usefulness and between perceived usefulness and behavioral intention, either. In conclusion, use of mobile service can be promoted by developing various kinds of mobile service, designing device and interface to use mobile service more convenience and familiar, stimulating user's innovative characters and recognizing mobile service more useful in real life.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.87-97
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• 2019

Reaction of people is importantly considered about specific case as a social network service grows. In the previous research on analysis of social network service, they predicted tendency of interesting topic by giving scores to sentences written by user. Based on previous study we proceeded research of sentiment analysis for social network service's sentences, which predict the result as positive or negative for movie reviews. In this study, we used movie review to get high accuracy. We classify the movie review into positive or negative based on the score for learning. Also, we performed embedding and morpheme analysis on movie review. We could predict learning result as positive or negative with a number 0 and 1 by applying the model based on learning result to social network service. Experimental result show accuracy of about 80% in predicting sentence as positive or negative.

• Design Convergence Study
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• v.15 no.2
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• pp.301-317
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• 2016

Flexible display interface is capable of creating new user behaviors based on its characteristics of outstanding surface representation and display transformations such as bending, rolling, and folding. Thus, it is being discussed that the newly emerging flexible display interface can offer a different user experience that the previous flat display couldn't. However, as it is hard to find studies that identify the general attributes of user experience in the area of flexible display research, this study was intended to identify and label experience that users expect in a flexible display interface. For this purpose, this study first investigated literature reviews about user experience in previous digital media interface and flexible display interface and conducted interview research in order to reflect the users' perception, collecting 52 items that represent user experience. In addition, these items were used as measurements to deduct different types of user experience, and 308 interviewees participated in the interview research process. As a result, 10 types of user experience were developed from the results of the survey: functionality, understandability, pleasure, convenience, familiarity, stimulation, adaptability, collectivity, reality, and aesthetic.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.517-528
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• 2016

This study attempted to explore and examine a new user experience (UX) research method for IoT products which are becoming widely used but lack practical user research. While user experience research has been traditionally opted for survey or observation methods, this paper utilized big data analysis method for user online reviews on an intelligent agent IoT product, Amazon's Echo. The results of topic modelling analysis extracted user experience elements such as features, conversational interaction, and updates. In addition, regression analysis showed that the topic of updates was the most influential determinant of user satisfaction. The main implication of this study is the new introduction of big data analysis method into the user experience research for the intelligent agent IoT products.

• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.35-56
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• 2023

With the development of information and communication technology, numerous reviews are continuously posted on websites, which causes information overload problems. Therefore, users face difficulty in exploring reviews for their decision-making. To solve such a problem, many studies on review helpfulness prediction have been actively conducted to provide users with helpful and reliable reviews. Existing studies predict review helpfulness mainly based on the features included in the review. However, such studies disable providing the reason why predicted reviews are helpful. Therefore, this study aims to propose a methodology for applying eXplainable Artificial Intelligence (XAI) techniques in review helpfulness prediction to address such a limitation. This study uses restaurant reviews collected from Yelp.com to compare the prediction performance of six models widely used in previous studies. Next, we propose an explainable review helpfulness prediction model by applying the XAI technique to the model with the best prediction performance. Therefore, the methodology proposed in this study can recommend helpful reviews in the user's purchasing decision-making process and provide the interpretation of why such predicted reviews are helpful.