• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.49-61
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• 2020

Recently, the convergence of genre-mixed collaboration and technology-mixed collaboration in performance art strategically fuses different goals and different properties, satisfying anticipated demand, and developing into experimental forms that bring out convergence contents of new value. To supplement problems of convergence attempts and heighten levels of completion, the effective collaborative processes of media experts must be studied and improved. This thesis attempts to study effective collaborative production plans of convergence performance that correspond to the demands of the times through multimedia convergence performance prototypes(Live performance play + Pre-made digital animation). It categorizes performance production processes into pre-production and production and researches the effective collaborative production processes of convergence performances that utilize these two forms of media through work selection, production direction establishment, human resource constituents, production schedule plan establishment, visualization processes, and performance practices and rehearsals. Continuous research must be conducted based on convergence performance contents planning, changes in production methods, and an understanding of distinct characteristics among convergence contents for the industrial development systemization and vitalization of convergence contents to be made possible.

• 한국IT서비스학회:학술대회논문집
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• 2003.11a
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• pp.223-232
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• 2003

추천시스템을 설계하는 방법에는 크게 Content-Based Filtering 기법과 Collaborative Filtering 기법이 있다. 이 중 Collaborative Filtering 기법은 사용자가 아직 평가하지 못한 상품에 대한 예측값을 계산할 때, 나와 유사한 상품선호를 갖고 있는 사람들이 그 상품에 대해 평가한 점수를 활용하는 방법이다. 하지만 순수한 Collaborative Filtering 방법은 일반적으로 알려진 Data Sparsity의 문제, First Rater의 문제뿐만 아니라 예측값의 부정확성과 기하급수적 계산량의 증가로 실제구현이 어렵다는 문제점을 가지고 있다. 본 연구에서는 이러한 'Collaborative filtering' 시스템의 문제들 중 예측의 부정확성과 실제 구현의 어려움을 해결할 수 있는 방법으로 군집분석을 적용해 보았다. 특히 본 연구에서는 군집을 나눌 때, 실제 추천이 이루어지는 상품 도메인이 아닌, 그 상품도메인과 비슷한 선호의 기준을 가지고 선택하게 되는 '선택의 상관관계'가 높은 '이웃 상품도메인'에서 사용자들의 군집을 나누고 이를 실제 추천이 이루어지는 상품도메인에 적용하는 방식을 사용하였다.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.179-190
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• 2018

This study is about the influence of authors' centrality on research outcomes in a large-scale collaborative research network. Using the social network analysis method, five types of centralities were derived. Six research outcomes of individual researchers were also derived through bibliographic information of the social science field for the last 10 years. A multivariate regression analysis was conducted to examine the causal relationship between the centrality and research outcome, and the effect of centrality on research outcomes was found to be statistically significant. The result of this study shows that the revised citation and H-index significantly influenced the authors' centrality. This result can imply that the centrality of the researcher can expect a considerable influence of the thesis as well as a certain level of productivity. The meaning of this study is to analyze the effect of centrality on the research outcomes of the large-scale collaborative research network in the past decade, and is carefully to suggest a guideline in order to support new research information services for active researchers and the advancement of collaborative research. This study has its limitation for interpreting the diverse academic fields of the social sciences in a uniform way. In future study, it is necessary to conduct studies using various weighted indices for network centrality in order to measure the influence of research.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.51-63
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. There are several treatments to deal with the incomplete data problem such as case deletion and single imputation. Those approaches are simple and easy to implement but they may provide biased results. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.51-59
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• 2010

There have been proposed several movie recommendation algorithms based on Collaborative Filtering(CF). CF decides neighbors whose ratings are the most similar to each other and it predicts how well users will like new movies, based on ratings from neighbors. This paper proposes a new method to improve the result predicted by CF based on genres of the movies seen by users. The proposed method can be combined to the most of all existing CF algorithms. In this paper, a performance evaluation has been conducted between an existing simple CF algorithm and CF-Genre that is the proposed genre-based method added to the CF algorithm. The result shows that CF-Genre improves 3.3% in prediction performance over existing CF algorithms.

• Journal of Distribution Science
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• v.20 no.4
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• pp.51-63
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• 2022

Purposes: The purpose of this study is to develop and test a possible model that investigates the relationships between green investment, CSR disclosure, social collaboration initiatives, and firm distribution performance to deal with environmental change because it's become the major stakeholder since it affects increasingly global company performance index. Research methodology: In this study a quantitative method was adopted. The 220 respondents were owners and managers of manufacturing enterprises from Indonesia. The structural equation model (SEM) was used to test the hypotheses, and the Partial Least Square (SmartPLS) was used as the data analysis tool. Findings: The study's finding shows that green investment has a significant effect on CSR disclosure, and CSR disclosure has a positive relationship with social collaborative initiatives and the firm's distribution performance. Similarly, social collaborative initiatives also significantly impact a firm's distribution performance. Limitations: This study uses variables that are still abstract and have not been able to regress the dimensions contained there into conclusion variables for each antecedent variable. In addition, this study only used a sample with a small scope, namely Central Java Province, Indonesia. Contribution: The findings of this study contribute to the body of literature in the field of organizational management and support the agency and stakeholder theories. For the practical contribution, this study provides the way to build and implement green-based investment strategies as a competitive edge and improve firm's distribution performance.

• The Journal of Korean Association of Computer Education
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• v.21 no.5
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• pp.61-68
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• 2018

Collaborative filtering is a fundamental technique implemented in many commercial recommender systems and provides a successful service to online users. This technique recommends items by referring to other users who have similar rating records to the current user. Hence, similarity measures critically affect the system performance. This study addresses problems of previous similarity measures and suggests a new similarity measure. The proposed measure reflects the subjectivity or vagueness of user ratings and the users' rating behavior by using fuzzy logic. We conduct experimental studies for performance evaluation, whose results show that the proposed measure demonstrates outstanding performance improvements in terms of prediction accuracy and recommendation accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2C
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• pp.229-237
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• 2010

The conventional detection methods developed for spatially-multiplexed MIMO systems such as OSIC and QRD-M show performance difference for each user depending on the order of detection when they are applied to detection of multi-user signals in uplink multiuser systems based on collaborative spatial multiplexing. In this paper, a signal detection method for uplink multiuser systems based on collaborative spatial multiplexing is proposed to provide similar performance for each user while its performance is close to the case of ML detection. Compared with QRD-M method, computational complexity of the proposed signal detection method is similar in the case of QPSK, and significantly lower in the case of high modulation order with 16-QAM and 64-QAM.

• The Journal of Korean Association of Computer Education
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• v.19 no.1
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• pp.111-118
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• 2016

Measuring similarity in collaborative filtering-based recommender systems greatly affects system performance. This is because items are recommended from other similar users. In order to overcome the biggest problem of traditional similarity measures, i.e., data sparsity problem, this study suggests a new similarity measure that is the optimal combination of previous similarity and the value reflecting the number of co-rated items. We conducted experiments with various conditions to evaluate performance of the proposed measure. As a result, the proposed measure yielded much better performance than previous ones in terms of prediction qualities, specifically the maximum of about 7% improvement over the traditional Pearson correlation and about 4% over the cosine similarity.

• Asia pacific journal of information systems
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• v.16 no.3
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• pp.159-176
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• 2006

Collaborative filtering is one of the popular techniques for personalized recommendation in e-commerce. In collaborative filtering, user profiles are usually managed per product category in order to reduce data sparsity. Product diversification of Internet storefronts and multiple product category sales of e-commerce portals require cross-product category usage of user profiles in order to overcome the cold start problem of collaborative filtering. In this paper, we study the feasibility of cross-product category usage of user profiles, and suggest a method to improve recommendation performance of cross-product category user profiling. First, we investigate whether user profiles on a product category can be used to recommend products in other product categories. Furthermore, a way of utilizing user profiles selectively is suggested to increase recommendation performance of cross-product category user profiling. The feasibility of cross-product category user profiling and the usefulness of the proposed method are tested with real click stream data of an Internet storefront which sells multiple product categories including books, music CDs, and DVDs. The experiment results show that user profiles on a product category can be used to recommend products in other product categories. Also, the selective usage of user profiles based on correlations between subcategories of two product categories provides better performance than the whole usage of user profiles.