• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.879-889
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• 2010

In this paper I summarize the compiling method of culture satellite account which has been recommended by international statistic organizations, and I suggest the required statistics which is need to compile Korean culture satellite account. Culture satellite account is recommended to be composed of five modules and each module has to has its specific function respectively. The first module is macro-information module, the second module is quantity/volume output module, the third module is characterization module, the fourth module is targeted analysis/analytical module, and the fifth module is documentation module. Each module is recommended to contain cultural demand and supply statistics of respective module from the first module to the fourth module.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.774-779
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• 2007

최근 온라인 경매나 오픈 마켓 같은 온라인 전자상거래가 활발히 이루어지고 있다. 더불어 사용자가 PDA와 같은 단말기를 휴대하고 이동하는 동안 전자상거래가 이루어질 가능성이 높아지고 있다. 이러한 상황에서 온라인 전자상거래의 구매자와 판매자를 대신해서 협상을 수행하고 매매를 성사시키는 에이전트에 대한 연구가 시도되고 있다. 이를 확장하여 지능형 에이전트를 휴대형단말기에 적용함으로써, 모바일 환경에서 구매자와 판매자간의 전자상거래를 대행하는 협상 에이전트의 필요성이 부각되고 있다. 본 연구에서는 모바일 전자상거래환경에서 에이전트가 협상에 대한 합리적인 판단할 수 있도록 게임이론을 사용하여 모델화 하였고, 휴대형단말기가 블루투스를 통해 인근에 있는 상점을 탐색 및 블루투스 네트워크를 구성하도록 하였다. 또한, 사용자가 협상 항목을 단계적으로 변경할 수 있는 전략을 사용할 수 있게 하였으며, 이를 평가함수를 사용하여 협상해서 최종 거래 결정은 자동 또는 수동으로 성사되게 하였다. 기존 협상 모델은 가격과 배송 방법 같은 협상 항목에 대해 가중치만을 변경하여 만족도를 판단함으로써, 실질적인 항목의 변화는 없고 초기에 설정된 협상 항목이 변경되지 않는 문제점이 있었다. 본 연구에서는 다단계 전략을 사용하여 단계마다 사용자의 요구 사항을 변경시킬 수 있기 때문에, 초기에 설정한 협상 항목의 값이 단계별로 변경될 수 있어 사용자의 의도가 협상에 실질적으로 반영되는 효과가 있다. 더불어 오프라인 전자상거래에서는 구매자가 직접 상점을 방문하여 상품을 검색하고 협상하였다. 그러나, 본 연구에서는 이동중인 사용자의 인근에 위치한 상점 에이전트로부터 상품 정보를 받는다. 그 후에 에이전트가 구매 협상을 하기 때문에 사용자가 이동 중에 상가에 직접 들어가지 않고도 상품을 검색 및 협상 할 수 있는 장점이 있다. 본 논문에서는 제안한 모바일 쇼핑 에이전트를 구현하고, 협상 과정과 결과를 비교하여 모델의 타당성과 성능을 평가한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.495-498
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• 2005

The study is aimed at searching algorithm upgrading which can automatically compose goods displayed according to the degree of popularity regarding customer's requests, for the purpose of design of an intellectual shopping mall on the net and putting it into force by using classified technical Data-mining and statical analysis including personal information , entrance records and purchase records. This is for the study of strategic marketing. The system can automate the conventional shopping mall system by manual and personal judgements and also suggest a new formation of marketing techniques to strengthen the competition in B2B market which is steeply increasing.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.67-69
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• 2005

본 논문에서는 기 구축된 전자 카탈로그를 의미적 연결 관계에 기초한 확장된 전자 카탈로그로 변환하는 방법을 제안한다. 이를 통해 구축된 확장된 전자 카탈로그에서 의미적 태깅에 의한 확장된 어휘 인덱스 구축 방안과, 이를 이용한 검색 성능 향상 기법을 제안한다. 기존의 전자 카탈로그는 상품 정보가 분류별로 생성된 테이블에 저장되고 저장된 테이블로부터 생성된 키워드 인덱스로부터 검색이 이루어 졌다. 이러한 검색은 상품이 가지는 정보를 데이터베이스에 구축된 테이블에만 한정하게 되어 전자 카탈로그에 포함된 상품이나 분류간의 의미적 연결 관계들을 충분히 이용하지 못하였다 전자 카탈로그에 내재된 의미적 요소를 충분히 활용하기 위해서는 전자 카탈로그를 의미적 연결 관계에 기초한 모델로 구성할 필요가 있다. 본 논문에서는 의미적 모델 기반 전자 카탈로그 시스템으로의 전환 과정을 XML형태의 명세를 이용해 반자동적으로 전환할 수 있는 툴을 구현하며, 단순 키워드 어휘 인덱스 구축이 아닌, 어휘 인덱스의 의미적 확장을 제안하고, 이를 위한 태그 요소로써 어휘에 대한 형태소 분석 결과, 수치 환산 및 확장 요소, 속성간의 도메인 정보 등을 제시하였다. 이를 기반으로 최적의 검색 결과를 얻어 내도록 하는 인접도 평가 함수에 적용하는 방법을 제시한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.2982-2990
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• 2011

The puporse of this research is to analyze the impacts of derivatives to firm value. The sample of this research consists of 20 domestic life insurance companies and the duration of the research is the year between 2002 and 2009. And multi-regression analysis by cross-sectional analysis approach is used for the entire sample in this study. The result of the research indicates the impact of derivatives use on the value of the firm, which was the original focus of this study, is insignificant. And firm value increases as the leverage, rate of return on a loan, the ratio of product for annuity and the ratio of expense decrease.

• Journal of Convergence for Information Technology
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• v.9 no.10
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• pp.56-63
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• 2019

The purpose of this paper is to derive an optimal route for foreign tourists in Korea. To that end, the data gained from domestic tourist portal sites was analyzed with a big data analytics tool R. The destinations most visited by inbound foreign tourists, the shortest route and the most economical route were derived from the analysis results. The findings suggest original Korean culture is the factor for successful tourist destinations and relevant products, and will serve as some reference data conducive to planning the tourist products in practice.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.183-200
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• 2009

Collaborative Filtering is one of the most used recommender systems. However, basically it cannot be used to recommend new products to customers because it finds products only based on the purchasing history of each customer. In order to cope with this shortcoming, many researchers have proposed the hybrid recommender system, which is a combination of collaborative filtering and content-based filtering. Content-based filtering recommends the products whose attributes are similar to those of the products that the target customers prefer. However, the hybrid method is used only for the limited categories of products such as music and movie, which are the products whose attributes are easily extracted. Therefore it is essential to find a more effective approach to recommend to customers new products in any category. In this study, we propose a new recommendation method which applies centrality concept widely used to analyze the relational and structural characteristics in social network analysis. The new products are recommended to the customers who are highly likely to buy the products, based on the analysis of the relationships among products by using centrality. The recommendation process consists of following four steps; purchase similarity analysis, product network construction, centrality analysis, and new product recommendation. In order to evaluate the performance of this proposed method, sales data from H department store, one of the well.known department stores in Korea, is used.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Journal of Distribution Research
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• v.7 no.2
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• pp.89-105
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• 2003

과거 할인점에서의 상품구색은 대체로 가격이 저렴한 제품들로 구성되어 있었지만, 외국계 할인점이 국내에 진출하고 할인점들 간 경쟁이 치열해지면서 할인점은 매장을 고급스럽게 디자인하고 상품가치가 높고 지명도가 높은 제품들로 구색을 갖추고 있다. 이러한 할인점의 새로운 위상정립에 따라 할인점에서의 화장품 매대도 진열과 구색이 변화되고 있는데, 본 연구는 할인점에서의 화장품 매대 및 취급상표의 특징, 그리고 화장품 매대를 쇼핑하는 소비자의 행태에 관한 자료를 수집 및 분석한 후, 그 결과를 종합하여 메이커와 소비자 측면에서 할인점을 통한 화장품 판매현황을 분석하였다. 조사결과 소비자들은 화장품 구매 장소로서 할인점을 기존 화장품 전문점보다 모든 측면에서 우수한 구매 장소로 인식하고 있었고, 백화점에 비해서도 나쁘지 않은 것으로 평가하고 있었다. 또한 메이커들도 할인점의 화장품매장에 대한 지원을 강화하고 있고, 할인점 전용상표를 개발하여 일반 화장품 전문점 및 백화점과 차별화된 유통경로를 구축하기 위하여 노력하고 있음이 확인되었다.