• Journal of Digital Convergence
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• v.13 no.1
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• pp.439-447
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• 2015

The game industry has a very important position. However, a problem with the game rating and legal issues so called "Internet Game Shutdown System" is with significant impact. So, this study looking for many information on this issue, over the course of the study of solutions was looking for a way to contribute to the game industry. The status of the domestic game industry and the institutional and legal problems were studied. In this study, for the development of the game industry, the government's policy support is needed. And game related-government agencies should be the maintenance of law and institutions and reasonable arrangements for the work of government agencies.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.939-944
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• 2002

Recently Korean Industrial Standards has been revised and established newly accordance with the ISO system, especially ISO 140 series. This study aims to introduce and review ISO 11654 which contents rating of sound absorption, and then this study suggests to establish appropriate evaluating method and Korean Industrial Standard of the sound absorber for use in building.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.19-36
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• 2015

Social media is an emerging issue in content services and in current business environment. YouTube is the most representative social media service in the world. YouTube is different from other conventional content services in its open user participation and contents creation methods. To promote a content in YouTube, it is important to understand the diffusion phenomena of contents and the network structural characteristics. Most previous studies analyzed impact factors of contents diffusion from the view point of general behavioral factors. Currently some researchers use network structure factors. However, these two approaches have been used separately. However this study tries to analyze the general impact factors on the view count and content based network structures all together. In addition, when building a content based network, this study forms the network structure by analyzing user comments on 22,370 contents of YouTube not based on the individual user based network. From this study, we re-proved statistically the causal relations between view count and not only general factors but also network factors. Moreover by analyzing this integrated research model, we found that these factors affect the view count of YouTube according to the following order; Uploader Followers, Video Age, Betweenness Centrality, Comments, Closeness Centrality, Clustering Coefficient and Rating. However Degree Centrality and Eigenvector Centrality affect the view count negatively. From this research some strategic points for the utilizing of contents diffusion are as followings. First, it is needed to manage general factors such as the number of uploader followers or subscribers, the video age, the number of comments, average rating points, and etc. The impact of average rating points is not so much important as we thought before. However, it is needed to increase the number of uploader followers strategically and sustain the contents in the service as long as possible. Second, we need to pay attention to the impacts of betweenness centrality and closeness centrality among other network factors. Users seems to search the related subject or similar contents after watching a content. It is needed to shorten the distance between other popular contents in the service. Namely, this study showed that it is beneficial for increasing view counts by decreasing the number of search attempts and increasing similarity with many other contents. This is consistent with the result of the clustering coefficient impact analysis. Third, it is important to notice the negative impact of degree centrality and eigenvector centrality on the view count. If the number of connections with other contents is too much increased it means there are many similar contents and eventually it might distribute the view counts. Moreover, too high eigenvector centrality means that there are connections with popular contents around the content, and it might lose the view count because of the impact of the popular contents. It would be better to avoid connections with too powerful popular contents. From this study we analyzed the phenomenon and verified diffusion factors of Youtube contents by using an integrated model consisting of general factors and network structure factors. From the viewpoints of social contribution, this study might provide useful information to music or movie industry or other contents vendors for their effective contents services. This research provides basic schemes that can be applied strategically in online contents marketing. One of the limitations of this study is that this study formed a contents based network for the network structure analysis. It might be an indirect method to see the content network structure. We can use more various methods to establish direct content network. Further researches include more detailed researches like an analysis according to the types of contents or domains or characteristics of the contents or users, and etc.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• 한국디지털정책학회:학술대회논문집
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• 2004.05a
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• pp.195-204
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• 2004

Due to growth of diversified media, content screening is the definite procedures. The procedures of screening varies from country by country in various reasons. Therefore, reason of conducting such study is to compare & contrast screening process by countries. In order to clarify definition of terms that measures screening, "censorship" means "legislative filtering process prior to public appearance". In contrary "Rating and/or Classification" is defined opposite of it. After defining these terms, Screening is dignified into two distinctive measures, which are "legislative intereference" and "voluntary notification". Those two measures are again sub-categorized into eight distinctive operational definition. Utilizing those distinctive measures, our study has concluded as US, Japan and some laissez-faire countries use "voluntary notification" systems but in contrast China and Brunei use "legislative filtering" system.? Korea and Australia uses unique combination of both system. In order for Korea to adopt "voluntary notification system", legislative intereference must be weaken and develop strong "voluntary notification" system.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.19-28
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• 2017

Collaborative filtering (CF) is a system that interprets the relationship between a user and a product and recommends the product to a specific user. The CF model is advantageous in that it can recommend products to users with only rating data without any additional information such as contents. However, there are many cases where a user does not give a rating even after consuming the product as well as consuming only a small portion of the total product. This means that the number of ratings observed is very small and the user rating matrix is very sparse. The sparsity of this rating data poses a problem in raising CF performance. In this paper, we concentrate on raising the performance of latent factor model (especially SVD). We propose a new model that includes product similarity information and co occurrence information in SVD. The similarity and concurrence information obtained from the rating data increased the expressiveness of the latent space in terms of latent factors. Thus, Recall increased by 16% and Precision and NDCG increased by 8% and 7%, respectively. The proposed method of the paper will show better performance than the existing method when combined with other recommender systems in the future.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.227-235
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• 2005

Document filtering is a task of deciding whether a document has relevance to a specified topic. As Internet and Web becomes wide-spread and the number of documents delivered by e-mail explosively grows the importance of text filtering increases as well. In this paper, we treat document filtering problem as binary document classification problem and we proposed the News Filtering system based on the Bayesian Classifier. For we perform filtering, we make an experiment to find out how many training documents, and how accurate relevance checks are needed.

• Architectural research
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• v.20 no.4
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• pp.121-128
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• 2018

Given the barriers to implement green building rating systems, Building Information Modeling (BIM) was suggested as an effective solution integrating information into one model and saving substantial time to facilitate certification process. Synergies between BIM and Leadership in Energy and Environment Design (LEED), the most widely used rating system, have been researched for a few decades. This paper demonstrates literature review about the development of integration between BIM and LEED. The research focuses on synergies between BIM and Green Standard for Energy & Environmental Design (G-SEED) in Korea, as one of important strategies to mitigate greenhouse gas emission. The research compares LEED and G-SEED related items based on evaluation contents. The result manifests G-SEED and LEED share many common items in different degrees. Therefore, it is entirely possible for G-SEED and BIM to adapt same developing mode of LEED and BIM. Moreover, the study measures the potential of BIM in application process of G-SEED certification by investigation of credits in LEED and G-SEED can be earned by BIM. The results of paper indicate the documentation support LEED and G-SEED may be prepared directly, semi-directly and indirectly via sustainability analyses software in BIM.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.90-102
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• 2021

Technology evaluation is to assess the ability of technology commercialization entities to generate profits by using the subject technology, and domestic technology evaluation agencies have established and implemented their own evaluation systems. In particular, the recently developed technology evaluation model in the fields of marine and fishery does not sufficiently reflect the poor environment for technology development compared to other industries, so it does not pass the level of T4 rating, which is considered appropriate for investment. This is recognized as a challenge that occurs when the common evaluation indicators and evaluation scales used in other industries, and when the scoring system for T1 to T10 grading is similarly or identically utilized. Therefore, through this study, we intend to secure the appropriateness and reliability of the results of the comprehensive rating calculation by developing technology evaluation models and indicators that well explain the nine marine and fisheries industry classification systems. Based on KED and technology evaluation case data, AHP-based index weighting and Monte Carlo simulation-based rating system are applied and the results of case studies are verified. Through the proposed model, we aim to enhance the usability of R&D and commercialization support programs based on fast, convenient and objective evaluation results by applying to upcoming technology evaluation cases.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.191-199
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• 2015

The Journal of Digital Policy & Management. This space is for the abstract of your study in English. In this paper, we propose a u-convergence Tourist Information 3.0 System using Flow Theory. It generates a sense of u-challenge and u-skills to maximize the enjoyment of tourists is u-convergence Tourist Information 3.0. This is a challenge to good sense and adjust the rating of the Convergence Contents information quality(CCIQ) analysis to maximize the enjoyment of tourists. Convergence Contents information quality(CCIQ) of the conductive continuity of the content closed antecedents u-conductive sense, the tourist synchronization adequacy may generate data that can be analyzed. Content Information Quality of rating is the leading factor in the ability of the u-skill mastery of tourists, can generate data availability. The result is to create a meta-model is referred to as content information to reach the best quality maximize enjoyment. Design a sense of u-challenge the skill of the information quality of the tourist information content has the advantage of being able to identify the data formation has the pleasure of tourists. By applying to future national competent standard it is expected to maximize the enjoyment of the job.