• Annual Conference of KIPS
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• 2010.04a
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• pp.353-354
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• 2010

최근 사용자의 취향에 맞는 콘텐츠를 필터링하여 자동으로 추천하는 연구가 활발히 진행되고 있다. 참여형, 개방형, 공유형 서비스들의 증가와 함께 웹 3.0 시대에는 더욱 지능화되고 개인화된 서비스가 중요시되고, 이를 위한 맞춤형 정보 제공 연구가 필수적이다. 본 논문에서는 사용자 맞춤형 추천 방법의 대표적인 기술인 협업 필터링(Collaborative Filtering) 방안 분석에 대해 설명하고, 협업 필터링 방법의 적용 연구를 설명한다.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.185-194
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• 2010

In recommender systems which are used widely at e-commerce, collaborative filtering needs the information of user-ratings and neighbor user-ratings. These are an important value for recommendation in recommender systems. We investigate the in-formation of rating in NBCFA (neighbor Based Collaborative Filtering Algorithm), we suggest new algorithm that improve prediction accuracy of recommender system. After we analyze relations between two variable and Error Value (EV), we suggest new algorithm and apply it to fitted line. This fitted line uses Least Squares Method (LSM) in Exploratory Data Analysis (EDA). To compute the prediction value of new algorithm, the fitted line is applied to experimental data with fitted function. In order to confirm prediction accuracy of new algorithm, we applied new algorithm to increased sparsity data and total data. As a result of study, the prediction accuracy of recommender system in the new algorithm was more improved than current algorithm.

• Journal of Ship and Ocean Technology
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• v.8 no.4
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• pp.45-55
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• 2004

How to achieve engineering collaboration among diverse engineering activities is one of the key topics in manufacturing fields nowadays. The infrastructure for collaborative engineering is essential, and it can be realized by information technologies and intelligent engineering applications in digital environments. Digital Manufacturing is a technology to facilitate effective product developments and agile productions by computer models representing the physical and logical schema and the behavior of real manufacturing systems including products, processes and factories. A digital factory as a well-designed and integrated digital environment is incorporated in it. In this paper, digital manufacturing is recommended as a good strategy for collaborative engineering, especially in product developments and productions. By business process analysis and some case studies, we suggested sophisticated digital models are very useful to concurrent and collaborative engineering. It is expected that digital manufacturing is a very good strategy for achieving dramatic time and cost savings in many engineering activities of many manufacturing industries, including machinery, automotive and shipbuilding.

• Educational Technology International
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• v.23 no.1
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• pp.1-39
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• 2022

We study emotion regulation in a distant CABLe (Collaborative Argumentation Based-Learning) setting at university. We analyze how students achieve the group task of synthesizing the literature on a topic through scientific argumentation on the institutional Moodle's forum. Distinguishing anticipatory from reactive emotional regulation shows how essential it is to establish and maintain a constructive working climate in order to make the best out of disagreement both on social and cognitive planes. We operationalize the analysis of anticipatory emotional regulation through an analytical grid applied to the data of two groups of students facing similar disagreement. Thanks to sharp anticipatory regulation, group 1 solved the conflict both on the social and the cognitive plane, while group 2 had to call out for external regulation by the teacher, stuck in a cyclically resurfacing dispute. While the institutional digital environment did afford anticipatory emotional regulation, reactive emotional regulation rather occurred through complementary informal and synchronous communication tools. Based on these qualitative case studies, we draw recommendations for fostering distant CABLe at university.

• Korea Science and Art Forum
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• v.20
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• pp.307-314
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• 2015

Tele-Gesture is a tangible user interface(TUI) device that allows a user to physically point to a 3D object in real life and have their gestures play back by a robotic finger that can point to the same object, either at the same time, or at another point in time. To understand the extent of the gestures as new way of digital collaborative communication, collaboration situation and types were experimented as TUI implementations. The design prototype reveals that there is a rich non-verbal component of communication in the form of gesture-clusters and body movements that happen in an digital communication. This result of analysis can contribute to compile relevant contributions to the fields of communication, human behavior, and interaction with high technology through an interpretive social experience.

• Asia pacific journal of information systems
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• v.27 no.2
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• pp.126-138
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• 2017

The proliferation of items increased the difficulty of customers in finding the specific items they want to purchase. To solve this problem, companies adopted recommender systems, such as collaborative filtering systems, to provide personalization services. However, companies use only meaningful and essential data given the explosive growth of data. Some customers are concerned that their private information may be exposed because CF systems necessarily deal with personal information. Based on these concerns, we analyze the effects of the amount of information on recommendation performance. We assume that a customer could choose to provide overall information or partial information. Experimental results indicate that customers who provided overall information generally demonstrated high performance, but differences exist according to the characteristics of products. Our study can provide companies with insights concerning the efficient utilization of data.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.7-13
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• 2024

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.10a
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• pp.385-390
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• 1997

In recent years there has been an increasing emphasis on shortening design cycles for bringing products to market. This requires the development of computer aided engineering tools which allow analysts to quickly evaluate the effect of design changes on noise, vibration, and harshness. Statistical Energy Analysis (SEA) modeling is a valuable tool for predicting noise and vibration as SEA models are inherently simpler and more robust than deterministic models. SEA modeling can be combined with design sensitivity analysis (DSA) to identify design changes which give the largest performance benefit. This paper describes SEA modeling of an equipment cab. SEA predictions are compared to test data, showing good agreement. The use of design sensitivity analysis in improving cab design is then demonstrated.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• Human Ecology Research
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• v.55 no.4
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• pp.351-362
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• 2017

Collaborative consumption (CC) occurs in organized systems or networks in which participants conduct sharing activities in the form of renting, lending, trading, bartering, and swapping of goods, services, transportation solutions, space, or money. Information and communications technologies (ICTs) that have emerged with CC. CC is expected to alleviate social problems such as hyper-consumption, pollution, and poverty by lowering the cost of economic coordination. In this study, we investigate the influence of educational experience and expected benefits of CC participation (intended to using and providing CC) of Adolescent Consumers. The subjects for the study were 418 high school students. Data was analyzed through frequency analysis, mean, standard deviation, t-test, ANOVA, Pearson's correlation, and hierarchical multiple regression analysis using SPSS Win 21.0. The results of this study are as follows. First, the significant positive relationship found between CC participants (intent to use and provide CC), educational experience (home education, school education, and mass media) and expected benefits (social benefit, economic benefit, enjoyment, community effect, and reputation). Second, enjoyment, mass media, reputation, social benefit, home education and school education values were variables that influenced the using participation intention for CC. Third, the major variables influencing the providing participation intention CC were home education, enjoyment, gender, community effect, and mass media values.