• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.273-277
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• 2009

Recently user-generated contents have been drastically increased. In this paper, we introduce the user reputation which can be used to evaluate quality of the content they created. First we have composed a social network that is based on user activity. And we have developed the algorithm to evaluate the users' reputation using this social network.

• International Journal of Contents
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• v.5 no.1
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• pp.51-55
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• 2009

In this paper, we present a structure of personalized e-Learning system to study for a test formalized by uniform multiple-choice using on/off line mixed estimations as is the case of Driver :s License Test in Korea. Using the system a candidate can study toward the license through the Internet (and/or mobile instruments) within the personalized concept based on IRT(item response theory). The system accurately estimates user's ability parameter and dynamically offers optimal evaluation problems and learning contents according to the estimated ability so that the user can take possession of the license in shorter time. In order to establish the personalized e-Learning concepts, we build up 3 databases and 2 agents in this system. Content DB maintains learning contents for studying toward the license as the shape of objects separated by concept-unit. Item-bank DB manages items with their parameters such as difficulties, discriminations, and guessing factors, which are firmly related to the learning contents in Content DB through the concept of object parameters. User profile DB maintains users' status information, item responses, and ability parameters. With these DB formations, Interface agent processes user ID, password, status information, and various queries generated by learners. In addition, it hooks up user's item response with Selection & Feedback agent. On the other hand, Selection & Feedback agent offers problems and content objects according to the corresponding user's ability parameter, and re-estimates the ability parameter to activate dynamic personalized learning situation and so forth.

• International Journal of Contents
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• v.12 no.2
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• pp.31-36
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• 2016

The purpose of this research is to realize interactive art based on user's motions information using real time internet search terms. For this purpose, real-time search terms and related news information were extracted from three domestic and foreign portal sites, and the extracted information was used to generate content for interaction with the user. For interaction between the generated content and the user, a motion-based interactive technology that optimizes the intentions and experiences of the user was developed. A motion-based interactive system can be used to develop an immersive interface that induces user interest.

• The Journal of Information Systems
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• v.32 no.4
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• pp.29-50
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• 2023

Purpose The impact of online reviews is widely acknowledged, with extensive research focused on text-based reviews. However, there's a lack of research regarding reviews in video format. To address this gap, this study aims to explore the connection between company-sponsored product review videos and the extent of directive speech within them. This article analyzed viewer sentiments expressed in video comments based on the level of directive speech used by the presenter. Design/methodology/approach This study involved analyzing speech acts in review videos based on sponsorship and examining consumer reactions through sentiment analysis of comments. We used Speech Act theory to perform the analysis. Findings YouTubers who receive company sponsorship for review videos tend to employ more directive speech. Furthermore, this increased use of directive speech is associated with a higher occurrence of negative consumer comments. This study's outcomes are valuable for the realm of user-generated content and natural language processing, offering practical insights for YouTube marketing strategies.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.301-311
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• 2019

Digital convergence put a huge challenges for broadcast media in terms of integrating user generated content (UGC). Keeping this in mind, objective of the study is to explore the factors that affecting UGC's integration in Broadcast channel from developing countries' perspective. We explored how and why UGC are appearing in Bangladeshi Television news. In-depth interview was used and news editorial level staffs were selected from leading Bangladeshi television channels. Findings suggest that, state interventions in crisis events and lack of experiences to handle crisis reporting played a crucial role to incorporate UGC in television news bulletins. One of the significant findings is that, mere traditional guidelines and work policy of the media houses will not be enough to handle user generated content as well as citizen's participation in news media. Thus, we recommend to formulate a comprehensive user generated content integration policy in the context of Bangladesh.

• Information Systems Review
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• v.25 no.2
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• pp.1-30
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• 2023

This study analyzes and examines the travel behavior of Chinese people in Korea through a questionnaire survey of Chinese people who are long-term residents in Korea using Xiaohongshu App (RED). In this study, we add some variables to the MTEs (Memorable Tourism Experiences) model to analyze the travel behavior of Chinese people who are in Korea for a long period of time. We also chose to survey the users of Xiaohongshu App (RED), a popular software in recent years, and found the following findings in 240 valid questionnaires: (1) Scenery, Entertainment, and Informativeness have positive effects on people sharing travel experiences, while interaction does not. (2) Sharing travel experiences had a positive effect on travel satisfaction and the intention to go to other destinations, and travel satisfaction had a positive effect on the intention to go to other destinations. This paper extends the literature on tourism by combining MTEs and UGC (User-Generated Content) models, and also provides relevant suggestions for further research on the travel behavior of foreigners in Korea.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.241-246
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• 2020

In this paper, we propose a user matching app based on hybrid app and a utilization plan to promote sports tourism in line with the growing trend of sports industry scale. The proposed app categorizes sports facilities across the country into regional, sports, private and public sports facilities to support reservations and matching. The proposed app applied a matching system in which matching scores were given according to the preference of events, places, and users by user net matching algorithm. Users can enjoy sports as a team or individual through the suggestion app even if they do not have any clubs or friends to which they belong. It can be used to improve tourism content services and establish tourism industry policies by analyzing data generated while using a user matching system.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.92-99
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• 2022

This study show the empirical results and the models that explain the content creator's personal brand value in the user-generated content platform. Producer's brand value performance could have enhancement and dilution by their activities for the long-term and repetitive change. Therefore, for the measure and analysis, the models have to catch the effect from producer's the diverse activities. This study would find the guideline by competitive analysis between (1) the impact of in-group user's self-motivated participation and (2) the impact of the social links from the outside platform. Based on the analysis results, producer's creation activity as focused on the specific and professional category increase their brand value for the long-term. However, producers would have to upload diverse category, after users are bored to their similar videos' as before. These empirical results would be a guidelines to the content management strategies for producers and the platform.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1541-1550
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• 2017

The increasing number of multimedia content offered to the user demands content recommendation. In this paper, we propose a system for recommending content related to the content that user is watching. In the proposed system, relationship information between content is generated using relationship information between representative keywords of content. Relationship information between keywords is generated by analyzing keyword collocation frequencies in Internet news corpus. In order to handle big corpus data, we design an architecture that consists of a distributed search engine and a distributed data processing engine. Furthermore, we store relationship information between keywords and relationship information between keywords and content in NoSQL to handle big relationship data. Because the query optimizer of NoSQL is not as well developed as RDBMS, we propose query optimization techniques to efficiently process complex queries for recommendation. Experimental results show that the performance is improved by up to 69 times by using the proposed techniques, especially when the number of requested related keywords is small.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.1-6
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• 2019

As the mobile market expands, a variety of platforms are available to provide multimodal media content. Multimodal media content contains heterogeneous data, accordingly, user requires much time and effort to select preferred content. Therefore, in this paper we propose multimodal media content classification using keyword weighting for recommendation. The proposed method extracts keyword that best represent contents through keyword weighting in text data of multimodal media contents. Based on the extracted data, genre class with subclass are generated and classify appropriate multimodal media contents. In addition, the user's preference evaluation is performed for personalized recommendation, and multimodal content is recommended based on the result of the user's content preference analysis. The performance evaluation verifies that it is superiority of recommendation results through the accuracy and satisfaction. The recommendation accuracy is 74.62% and the satisfaction rate is 69.1%, because it is recommended considering the user's favorite the keyword as well as the genre.