• Journal of Information Technology Applications and Management
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• v.11 no.1
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• pp.175-187
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• 2004

Many companies have integrated their internal processes with the aid of information technology. Now the focus is on interconnecting trading partners. Collaborative commerce (also known as c-Commerce) has a key role. It is not just about making it easier for business to communicate. It is about by breaking down barriers along the entire length of the supply chain. Some c-Commerce links are permanent, others get established when and where they are needed. c-Commerce is helping business in electronic supply chains to improve product delivery while cutting costs, to strengthen relationships and to reduce lead time. In this paper, we propose the concept of collaborative commerce, and design and implement Collaborative Commerce Portal (CCP) system of small and medium enterprise for marketing support. The technical definition of CCP is set of all technical elements can do collaborative commerce between small and medium enterprises with Internet or Internet. The target information of CCP includes usual and unusual information and all other information of companies used as process events. Through this, it enhances efficiency of business processes, core information sharing and marketing activation.

• Journal of the Korean housing association
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• v.12 no.2
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• pp.13-23
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• 2001

These data were acquired by 630 residents at Changwon city and Masan city in Kyungsang-namdo. 66.7% of them were positive or strongly positive on collaborative housings. This is presented the emerging housing needs because they were fed up with the present supply system of housings. They wanted that collaborative housings is low rising buildings and under 20 dwelling units, but rather to protect their own private spaces than to actualize community or common spaces. Sport facilities and parking spaces were the favourite spaces, but common dining room and laundry etc were disfavor spaces of community spaces. Now, to actualize the collaborative housings will be need the coordinate groups to have the special knowledge and experience.

• ETRI Journal
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• v.22 no.3
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• pp.27-40
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• 2000

Recently, advanced information technologies have opened new possibilities for collaborative designs. In this paper, a web-enabled collaborative design environment is proposed, where the product data based on STandard for the Exchange of Product model data (STEP) is managed in a hierarchical database and the product metadata is used to efficiently search and utilize information scattered over the network. Several integrity constraints are depicted using EXPRESS to validate the combination of data from different sources. The knowledge represented as metadata and constraints on the interacting features differentiate this environment from previous ones. The collaborative conferencing system is also introduced to communicate and collaborate simultaneously among the related designed. As a result, the proposed environment allows the distributed designers to more efficiently obtain, exchange and communicate the design information throughout the design process.

• Korean Journal of Computational Design and Engineering
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• v.6 no.3
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• pp.198-205
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• 2001

Under the concept of global economy, the enterprises are assigning design and production environments around the world in different areas. In shipbuilding companies, a serious problem of information exchange emerges as companies use traditional hardware and very distinct softwares appropriate to their field of expertise. To overcome the decreased productivity due to the interruption of information, the concept of simultaneous engineering and concurrent design becomes very significant. In this article, the concept of collaborative design based on internet environments is described. Especially, the core technologies to achieve collaborative design environments among shipbuilding companies, ship owners, ship classification societies, model basin, and consulting companies are adopted.

• Korean Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.31-39
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• 1998

This paper deals with a development of the system that implements a collaborative design environment with some intelligent agents on the m. Intelligent agents can design collaboratively trough an interchange of messages in their special domains. Such a collaborative design of agents is achieved on the WWW. In this paper, we propose special agents named intercessor and DCM (Dynamic Connection Manager) and suggest new connection architecture using these agents in the WWW in order to improve the pre-existed agent connection architecture. The proposed agents are developed using Java language and JATLite API. We apply the these agents to the new architecture and show some possibilities that the agent connection architecture can be extended in the WWW Agents interchange messages with others using KOML (Knowledge Query and Manipulation Language), agent communication protocol and language, and deal with message autonomously according to their rule base. Agents register and connect dynamically trough the intercessor agent, and infer from their.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5S
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• pp.1599-1608
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• 2000

Automated collaborative filtering is on the verge of becoming a popular technique to reduce overloaded information as well as to solve the problems that content-based information filtering systems cannot handle. In this paper, we describe three different algorithms that perform collaborative filtering: GroupLens that is th traditional technique; Best N, the modified one; and an algorithm that uses clustering. Based on the exeprimental results using real data, the algorithm using clustering is compared with the existing representative collaborative filtering agent algorithms such as GroupLens and Best N. The experimental results indicate that the algorithms using clustering is similar to Best N and better than GroupLens for prediction accuracy. The results also demonstrate that the algorithm using clustering produces the best performance according to the standard deviation of error rate. This means that the algorithm using clustering gives the most stable and the best uniform recommendation. In addition, the algorithm using clustering reduces the time of recommendation.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.121-126
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• 2016

In recommender systems based on collaborative filtering, measuring similarity is very critical for determining the range of recommenders. Data sparsity problem is fundamental in collaborative filtering systems, which is partly solved by Jaccard coefficient combined with traditional similarity measures. This study proposes a new coefficient for improving performance of Jaccard coefficient by compensating for its drawbacks. We conducted experiments using datasets of various characteristics for performance analysis. As a result of comparison between the proposed and the similarity metric of Pearson correlation widely used up to date, it is found that the two metrics yielded competitive performance on a dense dataset while the proposed showed much better performance on a sparser dataset. Also, the result of comparing the proposed with Jaccard coefficient showed that the proposed yielded far better performance as the dataset is denser. Overall, the proposed coefficient demonstrated the best prediction and recommendation performance among the experimented metrics.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.10-12
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• 2003

Recommender systems help consumers to find the useful products from the overloaded information. Researchers have developed content-based recommenders, collaborative recommenders, and a few hybrid systems. In this research, we extend the classic collaborative recommenders by clustering method to form a hybrid recommender system. Using the clustering method, we can recommend the products based on not only the user ratings but also other useful information from user profiles or attributes of items. Through our experiments on well-known MovieLens data set, we found that the information provided by the attributes of item on the item-based collaborative filter shows advantage over the information provided by user profiles on the user-based collaborative filter.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.151-153
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• 2017

In recommendation system, collaborative filtering is the most important algorithm. Collaborative filtering is a method of making automatic predictions about the interests of a user by collecting preferences or taste information from many users. In this paper five algorithms were used. Metrics such as Recall-Precision, FPR-TPR,RMSE, MSE, MAE were calculated. From the result of the experiment, the user-based collaborative filtering was the best approach to recommend movies to users.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.219-226
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• 2018

Collaborative filtering has been most popular approach to recommend items in online recommender systems. However, collaborative filtering is known to suffer from data sparsity problem. As a simple way to overcome this problem in literature, Jaccard index has been adopted to combine with the existing similarity measures. We analyze performance of such combination in various data environments. We also find optimal weights of factors in the combination using a genetic algorithm to formulate a similarity measure. Furthermore, optimal weights are searched for each user independently, in order to reflect each user's different rating behavior. Performance of the resulting personalized similarity measure is examined using two datasets with different data characteristics. It presents overall superiority to previous measures in terms of recommendation and prediction qualities regardless of the characteristics of the data environment.