• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.1-16
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• 1995

Group collaborative systems are recently emerging to support a group of users engaged in common tasks such as group decision making, engineering design, group scheduling, or collaborative writing. This paper provides an change management framework for the group collaborative system to facilitate managing dependency relationship between shared objects and dependent user views, and coordinating change and propagation activities between the two in distributed computing environments. Specifically, the framework adopts an object-oriented database paradigm and presents several object constructs capturing dependency management and change notification mechanisms. First, it introduces change management mechanisms with transient shared objects and secondly, it extends them into presistent object computing environment by integrating transaction management mechanisms and change notification mechanisms. A prototype change management framework is developed on a commercial object-oriented database management system.

• 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 Korea Society for Simulation Conference
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• 2001.05a
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• pp.163-163
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• 2001

The primary research questions in this paper are why and how competing firms collaborate, not compete, in the virtual marketplace, e.g., B2B marketplace in the Internet environment. In order to answer the questions, we take on a system dynamics simulation approach: we consider two broad e-collaboration strategies: · Exclusive e-business strategy If the firm adopts this strategy, it allocates all of its resources (available for e-business development) to its own e-business capability building only. · Collaborative e-business strategy When the firm adopts a collaborative e-business strategy, it invests not only in its own, but also the industrys e-business capability building. From the system dynamics simulation results, we conclude that e-collaboration pays off in the long run: although it is hard to tell whether the collaborative strategy is better than the exclusive one during the initial period, it is unambiguous that the collaborative e-business strategy Performs much better in the long run. We infer that such collaboration could occur when the firms realize that they benefit from the expansion of the market demand due to their collaboration. That is, in order for such collaboration between competing firms to be sustainable, such collaboration should create more demand in the market so that each company could earn more profit even if it gets less in terms of market share.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.109-114
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• 2018

Collaborative filtering has been widely utilized in recommender systems as typical algorithm for outstanding performance. Since it depends on item rating history structurally, The more sparse rating matrix is, the lower its recommendation accuracy is, and sometimes it is totally useless. Variety of hybrid approaches have tried to combine collaborative filtering and content-based method for improving the sparsity issue in rating matrix. In this study, a new method is suggested for the same purpose, but with different perspective, it deals with no-match situation in person-person similarity evaluation. This method is called the transitive similarity model because it is based on relation graph of people, and it compares recommendation accuracy by applying to Movielens open dataset.

• Korean Journal of Computational Design and Engineering
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• v.20 no.3
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• pp.254-262
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• 2015

This study proposes an approach to Product Data Management (PDM) education for collaborative product data management, which can support collaborative product development process. This approach introduces social media and a PDM system into a framework for PDM education supported by consistent product development process and product data model. It has been applied to two PDM classes and the result shows that the social media in PDM education can support not only experiences of the collaborative product data management but also interactive and informal communications among instructors and participants using integrated social media with product data during courses.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.351-356
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• 2001

Recently, advanced information technologies including Internet-related technology and distributed object technology have opened new possibilities for collaborative designs. In this paper, we discuss computer supports for collaborative design in a distributed environment. The proposed system is the Internet-centric system composed of an engineering framework, collaborative virtual workspace and engineering service. It allows the distributed designers to more efficiently and collaboratively work their engineering tasks throughout the design process.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.143-149
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• 2010

In ubiquitous environment, information retrieval using collaborative filtering is a popular technique for reducing information overload. Collaborative filtering systems can produce personal recommendations by computing the similarity between your preference and the one of other people. We integrate the collaboration filtering method and context-aware information retrieval method. The proposed method enables to find some relevant information to specific user's contexts. It aims to makes more effective information retrieval to the users. The proposed method is conceptually comprised of two main tasks. The first task is to tag context tags by automatic tagging technique. The second task is to recommend items for each user's contexts integrating collaborative filtering and information retrieval. We describe a new integration method algorithm and then present a u-commerce application prototype.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.11-17
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• 2018

In this paper, we propose how to improve the lack of diversity in collaborative filtering, using tag scores contained in items rather than ratings of items. Collaborative filtering has excellent performance among recommendation system, but it is evaluated as lacking diversity. In order to solve this problem, this paper proposes a method for supplementing diversity lacking in collaborative filtering by using tags. By using tags that can be used universally without using the characteristics of specific articles in a recommendation system, The proposed method can be used.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.159-162
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• 1997

Since customer's demand is various and product life cycle is getting shorter, many manufacturing company is trying to reduce product development time and cost. For that reason they make an effort to design product on collaborative environment. The various activities in a product development are highly distributed. This distributed nature of the activities implies that teams will be working indifferent place and technical environments. Thus at a given time, teams might work on he same product from different perspectives. This will require efficient communication amongst the various individuals and the various softwaretools that are used by them. Therefore, there is a need for a computerized frame work that can support distributed design such that participants from different backgrounds collaborate towards one common goal. This paper presents the architecture of a collaborative design system. It then reports on the study of computerized frame work focused on collaboration for product development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2310-2332
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• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.