• Proceedings of the CALSEC Conference
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• 1998.10a
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• pp.17-25
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• 1998

$\square$ 경쟁 원리에 따른 민간주도 추진 및 최소한의 정부 규제 (환경조성/수요창출 등) $\square$ 전자거래의 안전성, 신뢰성 확보 및 이용자의 권익 보호 (과세원칙/전자지급제도/전자서명, 인증, 암호화/지적소유권보호/소비자보호, 개인정보보호, 분쟁조정) $\square$ 글로벌화의 환경변화에 능동적으로 대응하는 유연한 법제도의 수립 및 시행 (과세 원칙 /전자지급제도) $\square$ 전자거래 기반에 대한 자유로운 현실적 접근. 활용 및 신뢰성ㆍ저렴화 선결 (전자거래기술개발 및 표준화) $\square$ 전자거래 관련 국제협력의 촉진 $\square$ 준비된 소비자 대응 및 네트워크 수요자를 대비한 1:1 마케팅 환경 조성 (집단적 의사대변) $\square$ 기술적 규제를 대비한 개도국 입장의 지속적인 요소기술 연구활동 촉진 및 EC 인프라 구축 $\square$ EC관련 규제체제와 기존 상거래와의 충돌 가능성 제거 및 흡수 (현실/규제의 융합) $\square$ EC 인프라 공동운영 및 거래의 수평적 협동 지원(중략)

• Proceedings of the Safety Management and Science Conference
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• 2012.11a
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• pp.87-92
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• 2012

본 연구는 전자상거래에서 협력적 필터링 알고리즘을 통한 사용자의 선호도 예측 정확도와 사용자가 평가한 선호도 평가치의 관계를 분석하여 알고리즘의 예측 정확도에 영향을 미치는 평가치의 통계적 특성에 관하여 연구한다. 협력적 필터링 알고리즘의 예측 정확도는 상품에 대해 공통의 관심을 갖는 이웃 사용자들의 선정과 이들의 선호도 경향이 중요한 요인이지만 본 연구에서는 선호도 예측을 위한 자신의 선호도 평가치 특성이 알고리즘에 중요한 요인임을 제시한다. 이러한 평가치의 평균, 표준편차, 왜도, 첨도 등과 같은 통계적 특성이 선호도 예측 정확도와 연관성이 있음을 제시하여 차후 연구에서 선호도 예측 이전에 사용자의 선호도 예측성과에 대한 사전평가의 가능성을 제시하고자 한다.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.505-514
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• 2009

The researcher analyzes the relationship between the number of neighbors and the prediction accuracy in the preference prediction process using collaborative filtering system. The number of neighbors who are involved in the preference prediction process are divided into four groups. Each group shows a little difference in the preference prediction. By using prediction error averages in each group, linear functions are suggested. Through the result of this study, the accuracy of preference prediction can be raised when using linear functions by using the number of neighbors in the suggested system.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.467-477
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• 2004

While recommender systems were used by a few E-commerce sites former days, they are now becoming serious business tools that are re-shaping the world of I-commerce. And collaborative filtering has been a very successful recommendation technique in both research and practice. But there are two problems in personalized recommender systems, it is First-Rating problem and Sparsity problem. In this paper, we solve these problems using the associative relation clustering and “Lift” of association rules. We produce “Lift” between items using user's rating data. And we apply Threshold by -cut to the association between items. To make an efficiency of associative relation cluster higher, we use not only the existing Hypergraph Clique Clustering algorithm but also the suggested Split Cluster method. If the cluster is completed, we calculate a similarity iten in each inner cluster. And the index is saved in the database for the fast access. We apply the creating index to predict the preference for new items. To estimate the Performance, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.1-8
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• 2007

In electronic commerce, it is important for users to recommend the proper item among large item sets with saving time and effort. Therefore, if the recommendation system can be recommended the suitable item, we will gain a good satisfaction to the user. In this paper, we proposed the associative relation clustering split method in the collaborative filtering in order to perform the accuracy and the scalability. We produce the lift between associative items using the ratings data. and then split the node group that consists of the item to improve an efficiency of the associative relation cluster. This method differs the association about the items of groups. If the association of groups is filled, the reminding items combine. To estimate the performance, the suggested method is compared with the K-means and EM in the MovieLens data set.

• Korean Business Review
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• v.17
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• pp.185-214
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• 2004

This paper is focused on the e-business level in Kangwon Province by industries, volumes, employees comparing with nation wide survey output, and analysis of the tasks and problems of e-business in environment, infrastructure, process, manpower, effect. Poor level of Kangwon province was identified. It is important that role clarity and cooperation among central goverment, Kangwon province, large enterprises and small and medium enterprises. Local goverment and regional cities and counties should play a centripetal role of the regional enterprises, and faithfully execute the support policies of the central goverment, and make the unique supporting climate of coopration between the university and enterprises in consideration with situations of each regional characteristics. More specifically, there are widening the infrastructure of e-business for regional enterprises, strenthening educatin and public relation of the electronic commerce, supporting and utilizing the electronic ccommerce research center. Also, fund supporting for SOHO is desirable in consideration of the poor Kangwon land and population, and to enhence the electronic commerce technology of the Kangwon enterprises, relative importance of policy of it is incresing. In conclusion, indices of Kangwon province in all areas of the survey is appeared poor. So efficient and effective policies is needed in each central and regional goverment. To promote Kangwon enterprises, investment in e-business commerce should be expanded.

• The Journal of Information Technology
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• v.7 no.1
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• pp.69-86
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• 2004

In this paper, a conceptual framework describing the dynamics of click and mortar businesses is provided. It directs our attention to the many potential sources of synergy that are available to firms that choose to integrate e-commerce with their existing traditional forms of business. It further emphasizes the many actions that firms can take to minimize channel conflicts and help achieve the benefits of synergy. Finally, it describes four categories of synergy-related benefits from the integration of e-commerce with traditional businesses, including potential cost saving, gains due to enhanced differentiation, improved trust, and potential extensions into new markets. The utility of the framework was demonstrated using the case of an electronics retailer that has chosen to tightly integrate its large chain of retail stores with its Web-based electronic store. The framework was also used to develop a series of propositions that can guide future empirical research. The discussion points to the need to develop new types of metrics to better judge the contributions of e-commerce channels, and provides some guidance for future empirical research that can test whether, and under what conditions, integrated click and mortar business models work well. Thus, the purpose of this study is to present the combining physical and virtual presence in e-commerce.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.625-632
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• 2001

The electronic stores have realized that they need to understand their customers and to quickly response their wants and needs. To be successful in increasingly competitive Internet marketplace, recommender systems are adapting data mining techniques. One of most successful recommender technologies is collaborative filtering (CF) algorithm which recommends products to a target customer based on the information of other customers and employ statistical techniques to find a set of customers known as neighbors. However, the application of the systems, however, is not very suitable for seasonal products which are sensitive to time or season such as refrigerator or seasonal clothes. In this paper, we propose a new adjusted item-based recommendation generation algorithms called the exponentially weighted collaborative filtering recommendation (EWCFR) one that computes item-item similarities regarding seasonal products. Finally, we suggest the recommendation system with relatively high quality computing time on main memory database (MMDB) in XML since the collaborative filtering systems are needed that can quickly produce high quality recommendations with very large-scale problems.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.123-133
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• 2002

오늘날 정보기술의 발달로 인해 기업들은 가상공간에 시장을 형성하여 전자적으로 거래를 하고 있다. 한편 거래에서 가장 중요한 과정은 협상이라고 할 수 있다. 따라서 현재의 거래환경과 새로운 거래환경을 지원하기 위한 전자상거래시스템에서의 협상기능은 매우 중요한 부분으로 생각된다. 이에 본 논문에서는 중소제조업을 대상으로 구매자와 판매자간의 협상과정을 지원하기 위한 에이전트의 구축방안을 판매자 측면에서 제시하고자 한다. 제조업체의 생산능력 한계로 인해 접수된 주문의 납기일을 준수할 수 없을 경우 제조업체는 구매자에게 납기일을 연장해 줄 것을 요청하게 되며 구매자는 가격을 낮추어 줄 것을 요구하게 된다. 납기일 정보를 얻기 위해 본 논문의 협상 에이전트는 일정계획 에이전트와 협력하고 있으며 협상 대상자가 다수일 경우에도 다자간 협상을 수행할 수 있도록 하였다.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.809-818
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• 2009

Collaborative filtering approach predicts the preference of active user about specific items transacted on the e-commerce by using others' preference information. To improve the prediction accuracy through collaborative filtering approach, it must be needed to gain enough preference information of users' for predicting preference. But, a bit much information of users' preference might wrongly affect on prediction accuracy, and also too small information of users' preference might make bad effect on the prediction accuracy. This research suggests the method, which decides suitable numbers of neighbor users for applying collaborative filtering algorithm, improved by existing k nearest neighbors selection methods. The result of this research provides useful methods for improving the prediction accuracy and also refines exploratory data analysis approach for deciding appropriate numbers of nearest neighbors.