• Journal of Distribution Science
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• v.12 no.3
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• pp.85-98
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• 2014

Purpose - This study primarily aims to represent the current trend of research on CPFR as a promising supply chain collaboration program and proposes a new framework for analyzing any collaboration programs in terms of three key collaborative features. Research design, data, and methodology - This study employs a literature review of selected studies that conduct research on CPFR. CPFR is analyzed based on the proposed framework that characterizes collaboration programs in terms of three key collaborative features. Results - The analysis based on the proposed framework reveals that the current form of CPFR continues to have some collaborative features that are not fully utilized to create an advanced collaboration program. The literature review indicates that most past studies ignore critical issues including the dynamic nature of the multiple-stage supply chain system and negotiation process for collaborative agreement in CPFR implementation. Conclusions - Results indicate that CPFR can become a better supply chain collaboration program by incorporating coordinative cost payment and joint decision making processes. Based on observations on the existing literature of CPFR, this study indicates several important issues to be addressed by future studies.

• Korean Journal of Computational Design and Engineering
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• v.9 no.1
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• pp.62-76
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• 2004

Many customers are no longer satisfied with mass-produced goods. They are demanding customization and rapid delivery of innovative products. Many companies are now realizing that the best way to reduce life cycle costs is to evolve a more effective product development paradigm using Internet and web based technologies. Yet there remains a gap between current market demands and product development paradigms. The existing CAD systems require that product developers possess all the design analysis tools in-house making it impractical to employ all the needed and newest tools. Hence, this paper addresses how assembly operation analysis can be embedded transparently and remotely into a service-oriented collaborative assembly design environment. A new assembly operation analysis framework is introduced and a relevant architecture and tools are developed to realize the framework. Instead of the current sequential process for verifying and validating an assembly design, a new Virtual Assembly Analysis (VAA) method is introduced in the paper to predict the various effects of joining during actual collaborative design. As a case study, arc welding and riveting processes are investigated. New service-oriented VAA architecture and its VAA components are proposed and implemented on prototype mechanical assemblies.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.3
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• pp.89-108
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• 2022

The purpose of this study is to reveal the specific current and future shapes of the collaborative network among organizations witch cope the COVID-19 in Korea. For this, this study conducted social network analysis, based on the response data of 73 experts from 36 COVID-19-related organizations. As a result of the analysis, it was confirmed that the Korea Disease Control and Prevention Agency (KDCA) plays a pivotal role as a control tower in coping COVID-19 in all of the analysis of degree, betweenness, and closeness centrality. In addition, the results revealed concrete forms of collaborative relationships among participating organizations in the public and private sectors that constitute the present and future networks centered on the KDCA. Furthermore, this study presented which organizations and relationships should be the focus of establishing a future collaborative network through comparative analysis between the current cooperative network and the network to be built in the future. The analysis results and discussions of this study are expected to be used as useful information for policy development related to collaborative networks that can effectively respond to disasters caused by new diseases in the future.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.3
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• pp.55-76
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• 2022

The 3rd Library Development Plan raises the need to secure space through the establishment of a regional repository library as the issue of holding books is highlighted as a common problem in public libraries. The Korean Library Law Act also impose the responsibility of integrated management of local library materials on the regional representative library. Accordingly, this study aimed at Busan Metropolitan Library, which is operating a collaborative repository in earnest among regional representative libraries, and investigated the operation status of the collaborative repository and the perception of public librarians about the collaborative repository. The data necessary for the study were obtained through surveys, interviews, field surveys, and internal data analysis. Through this, the purpose of this study was to provide basic data that will help the Busan Metropolitan Library to operate the collaborative repository efficiently in the future, and at the same time, to present basic data that can be used as a reference for the operation of the collaborative repository of the representative libraries of other regions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.970-977
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• 2020

The collaborative filtering recommendation technique has been the most widely used since the beginning of e-commerce companies introducing the recommendation system. As the online purchase of products or contents became an ordinary thing, however, recommendation simply applying purchasers' ratings led to the problem of low accuracy in recommendation. To improve the accuracy of recommendation, in this paper suggests the method of collaborative filtering that analyses product reviews and uses them as a weighted value. The proposed method refines product reviews with text mining to extract features and conducts sentiment analysis to draw a sentiment score. In order to recommend better items to user, sentiment weight is used to calculate the predicted values. The experiment results show that higher accuracy can be gained in the proposed method than the traditional collaborative filtering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.756-764
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• 2020

A typical algorithm used for image analysis is the Convolutional Neural Network(CNN). R-CNN, Fast R-CNN, Faster R-CNN, etc. have been studied to improve the performance of the CNN, but they essentially require large amounts of data and high algorithmic complexity., making them inappropriate for small and medium-sized services. Therefore, in this paper, the image label prediction algorithm based on CNN with collaborative layer with low complexity, high accuracy, and small amount of data was proposed. The proposed algorithm was designed to replace the part of the neural network that is performed to predict the final label in the existing deep learning algorithm by implementing collaborative filtering as a layer. It is expected that the proposed algorithm can contribute greatly to small and medium-sized content services that is unsuitable to apply the existing deep learning algorithm with high complexity and high server cost.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.439-450
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• 2010

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

• Asia pacific journal of information systems
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• v.14 no.2
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• pp.131-149
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• 2004

Collaborative filtering is one of popular techniques for personalized recommendation in e-commerce sites. An advantage of collaborative filtering is that the technique can work with sparse evaluation data to predict preference scores of new alternative contents or advertisements. There is, however, no in-depth study about the sparsity effect of customer's evaluation data to the performance of recommendation. In this study, we investigate the sparsity effect and hybrid usages of customers' evaluation data and purchase data using an experiment result. The result of the analysis shows that the performance of recommendation decreases monotonically as the sparsity increases, and also the hybrid usage of two different types of data; customers' evaluation data and purchase data helps to increase the performance of recommendation in sparsity situation.

• Agribusiness and Information Management
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• v.3 no.1
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• pp.1-10
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• 2011

In this exploratory analysis, we investigate the genesis and the evolution of local food-purchasing networks created and operated by consumers. In details, we describe how collecting and sharing information about food-products can become a central activity for some consumers' communities and how these communities are starting to play an active role in the food supply chain. We define this community-based food-purchasing model as collaborative food network (CFN), and we analytically describe its characteristics and differences with respect to the traditional and industrialized agrifood supply chain models. A collaborative food network community in Italy, known as GAS ("Gruppi di Acquisto Solidale" - "Solidarity Purchasing Groups"), is introduced as an example of our analytical model. We will use this empirical example to present the strengths and weaknesses of the CFN model.