• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.197-214
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• 2006

This study explores factors to facilitate web-based collaborative learning and the effect of learning, based on the PBL(Problem Based Learning) from the constructivist approach in e-learning. A research model, using the key variables such as situations, interactions, and systems, was developed. In order to test this proposed model, experimental design and post-survey was conducted to the learners who took on-line and off-line course with team project. In the research model, situation category was divided into instructor's support, unstructured problem, and self-directed learning. Interaction category was divided into three factors; 'interaction between learners', 'interaction between learner and instructor', and 'interaction between learner and technology'. System category was divided into.monitoring and incentives. As a result, it was found that collaborative learning can be improved by situations, interactions, and systems, and the effectiveness of learning can be improved by situations and interactions in PBL.

• Journal of Ocean Engineering and Technology
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• v.18 no.6 s.61
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• pp.37-43
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• 2004

Under the concept of the global economy, the enterprises are assigning design and production environments around the world in various areas. In shipbuilding companies, a serious problem of information exchange has emerged as companies use traditional hardware and very distinct softwares that is tailored to their field of expertise. To overcome the decreased productivity resulting from the interruption of information, the concepts of simultaneous engineering and concurrent design become very significant. In this article, the concept of collaborative design based on Internet environments is described. Specifically, the core technologies necessary to achieve collaborative design environments among shipbuilding companies, ship owners, ship classification societies, model basin, and consulting companies are adopted.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.228-230
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• 2020

The emergence of collaborative learning to the public is to tackle the user's privacy issue in centralized learning by bringing the AI models to the data source or client device for training Collaborative learning employs computing and storage resources on the client's device. Thus, it is privacy preserved by design. In harmony, blockchain is also prominent since it does not require an intermediary to process a transaction. However, these approaches are not yet fully ripe to be implemented in the real world, especially for the complex system (several challenges need to be addressed). In this work, we present the performance of collaborative learning and potential use case of blockchain. Further, we discuss privacy issues in the system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.364-367
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• 2000

As computer system and information/communication technology develop rapidly, nowadays CSCW(Computer Supported Collaborative Work) system appears, through which it is available to work on virtual space without any restriction of time and place. Most of Un systems depend on a special network and groupware. The systems of graphics and CAD are not so many because they are characterized by Hardware and application software. In this paper, we propose a web-based collaborative CAD system which can be jointly worked on Internet WWW being independent from any platforms. It can generate and handle objects easily using lava 3D, and it can transmit, print, and store them. The interactive work for designing objects can be also carried out through dialling with each other. This system is executed in the environment of Client/server. Clients connect to the server through lava applet on WWW. The server is implemented by lava application, and it consists of three components : connection manager which controls the contact to users, work manager which keeps viewing in concurrency and provides virtual work space sharing with others, and solid modeler which generates 3D object.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.599-600
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• 2006

In order to promptly cope with the various requirements of consumers, the environment of product development is being globalized in manufacturing industrials. For this reason, it is necessary to build up an efficient collaborative system for communication between remote area designers. Specially, while Internet and information technologies were merged with the manufacturing or business process, the research for collaborative system has become an important issue. Therefore, we propose a Web-based Engineering Collaboration Framework using SPS(SharePoint Portal Server) which is an enterprise business solution that integrates information from various system into one solution through single sign-on and enterprise application integration capabilities, with flexible deployment options and management tool. Through a Web-based Engineering Collaboration Framework, designers can share knowledge assets and have a remote conference with others via web.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.125-131
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• 2017

It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.

• The Journal of Information Systems
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• v.17 no.3
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• pp.135-152
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• 2008

Basically within a special regional area, a regional cluster seems to be based on core competencies of individual intra-cluster companies and collaboration among them. Information infrastructure has been emphasized as on one of competitive factors of a regional cluster, and it can be organized using collaboration system architecture integrated with each company's internal systems for efficient supply chain operation. As one of technical methods to prepare the system infrastructure supporting the collaboration of companies in a regional cluster, the Web Services can be effectively used. In this paper, a collaborative information system infrastructure for a regional cluster is suggested within the scope of supply chain management. And the efficiency of the proposed alternative is appraised with the features of a regional cluster.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.289-296
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• 2015

The explosive growth of information has been difficult for users to get an appropriate information in time. The various ways of new services to solve problems has been provided. As customized service is being magnified, the personalized recommendation system has been important issue. Collaborative filtering system in the recommendation system is widely used, and it is the most successful process in the recommendation system. As the recommendation is based on customers' profile, there can be sparsity and cold-start problems. In this paper, we propose personalized movie recommendation system using collaborative filtering techniques and context-based techniques. The context-based technique is the recommendation method that considers user's environment in term of time, emotion and location, and it can reflect user's preferences depending on the various environments. In order to utilize the context-based technique, this paper uses the human emotion, and uses movie reviews which are effective way to identify subjective individual information. In this paper, this proposed method shows outperforming existing collaborative filtering methods.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.101-108
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• 2020

Recommender system is a must-have feature of e-commerce, since it provides customers with convenience in selecting products. Collaborative filtering is a widely-used and representative technique, where it gives recommendation lists of products preferred by other users or preferred by the current user in the past. Recently, researches on the recommendation system using deep learning artificial intelligence technologies are actively being conducted to achieve performance improvement. This study develops a collaborative filtering based recommender system using restricted Boltzmann machines of the deep learning technology by utilizing user ratings. Moreover, a learning parameter update algorithm is proposed for learning efficiency and performance. Performance evaluation of the proposed system is made through experimental analysis and comparison with conventional collaborative filtering methods. It is found that the proposed algorithm yields superior performance than the basic restricted Boltzmann machines.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.6
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• pp.253-260
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• 2015

When we buy items in online stores, it is common to face recommended items that meet our interest. These recommendation system help users not only to find out related items, but also find new things that may interest users. Recommendation system has been widely studied and various models has been suggested such as, collaborative filtering and content-based filtering. Though collaborative filtering shows good performance for predicting users preference, there are some conditions where collaborative filtering cannot be applied. Sparsity in user data causes problems in comparing users. Systems which are newly starting or companies having small number of users are also hard to apply collaborative filtering. Content-based filtering should be used to support this conditions, but content-based filtering has some drawbacks and weakness which are tendency of recommending similar items, and keeping history of a user makes recommendation simple and not able to follow up users preference changes. To overcome this drawbacks and limitations, we suggest weighted window assisted user history based recommendation system, which captures user's purchase patterns and applies them to window weight adjustment. The system is capable of following current preference of a user, removing useless recommendation and suggesting items which cannot be simply found by users. To examine the performance under user and data sparsity environment, we applied data from start-up trading company. Through the experiments, we evaluate the operation of the proposed recommendation system.