• CRM연구
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• v.4 no.2
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• pp.19-29
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• 2011

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.264-273
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• 2003

인터넷의 발달로 전자 상거래는 오늘날 일상생활의 한 부분이 되었다. 그러나, 수많은 쇼핑몰들과 그 쇼핑몰들이 제공하는 다양한 제품들 속에서 소비자가 원하는 물건을 찾아내는 것은 점점 많은 시간과 노력이 필요하게 되었다. 본 논문에서는 멀티에이전트 시스템을 이용해 공급망(Supply Chain)에서 구매자의 요구에 부합하는 제품을 제공할 수 있는 생산자를 보다 쉽게 연결시켜주는 방법을 제안한다. 기존의 멀티 에이전트 기반 공급망에서 주로 사용되는 협동 전략인 Joint Intention Theory와 SharedPlan Theory, 이 논문에서 제안하는 신경망을 이용한 방법을 비교해, 신경망을 이용한 방법이 갖는 효율성을 알아보고, 신경망을 이용한 멀티에이전트 기반의 협등 모델을 제시하였다. 이 모델은 구매자가 제품을 선택할 때 사용하는 소비평가 기준의 가중치를 소비자로부터 받아들여 그 기준에 가장 부합하는 판매자를 신경망을 이용한 분류(classification)방법을 통해 찾아내고, 이렇게 선택된 생산자를 소비자에게 연결시켜준다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.699-702
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• 2003

Most of current shopping malls do not satisfy everyone because they present arrangements of goods and suggestions uniformly and comprehensively according to the thinking of their managers. On the other hand not the standard of selection but the comparison of price plays a decisive role of the purchase of goods as similar form each other. When classifying into groups according to generations, gender, income, job, hobby, etc. the propensity of purchase is showed differently and the interest and real purchasing power of the individual is different in shopping malls. It also will maximize the purchasing power of customers to make and implement the sales strategy more quickly as the basis of fashion and season of environmental factors and natural calamity of environmental variable according to the economic principle. This paper concentrates on the design and implementation of intelligent shopping mall that is added the sales strategy according to environmental variable and can not only analysis, update and classify the propensity of purchase continuously but also construct optimal goods automatically.

• Journal of the Korea Fashion and Costume Design Association
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• v.15 no.3
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• pp.51-67
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• 2013

This study aims to examine impacts of the Internet shopping orientation of Korean and Chinese consumers on their selection criteria of shopping malls and apparel products, frequencies and amounts of purchase, and information search in the Internet shopping mall. A survey was carried out with Korean and Chinese female consumers in their 20s and 30s who have the Internet shopping experiences. The results of this study were as follows: First, the Internet shopping orientation of Korean and Chinese respondents clearly showed factorial structures including the pleasure-conscious, fashion-conscious, price-conscious, and convenience-conscious orientation. From a result of the cluster analysis on four factors of the Internet shopping orientation, Korean and Chinese respondents were classified into three groups of the Internet shopping-unconscious, the Internet shopping-loyalty, and pleasure convenience-conscious. Second, there were significant differences in selection criteria of both the Internet shopping mall and apparel products among three groups of the Internet shopping orientation in Korea and China. Third, significant differences were identified in visiting frequencies, apparel purchase frequencies and amounts, payment methods, and information sources during the Internet shopping among three groups of the Internet shopping orientation in Korea and China.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.2
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• pp.179-189
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• 2008

The purposes of this study were to segment surrogate internet shopping mall consumers by fashion leadership and to find the differences among the segmented groups in regard to surrogate internet shopping perceived risks, selection criteria, dissatisfactions with surrogate shopping malls, and other purchase behavior. The subjects of this study were female consumers who were users of surrogate internet shopping malls. The data were collected during October, 2005. The respondents returned the questionnaires and 283 questionnaires were finally used in the data analysis. The statistical analyses used for the study were factor analysis, ANOVA, Duncan test, and $X^2$-test. The results showed that consumers were segmented by four groups: fashion dual leaders, fashion leaders, fashion followers, and fashion laggards. These segmented groups were significantly different in regard to surrogate internet shopping mall perceived risks, selection criteria, dissatisfactions with surrogate shopping malls, and other purchase behavior. Generally, fashion dual leaders had less perceived risks, considered diverse selection criteria important, and were less dissatisfied with surrogate shopping malls. Also, the fashion dual leaders had a higher purchase frequency and paid a higher price on surrogate internet shopping malls.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.605-610
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• 2007

사례기반추론(CBR)은 많은 장점으로 인해, 생산, 재무, 마케팅 등의 분야의 다양한 경영의사결정문제 해결에 적용되어 왔다. 그러나, 효과적인 CBR 시스템을 설계, 구축하기 위해서는 연구자가 직관적으로 설정해야 할 많은 변수들이 존재한다. 본 연구에서는 이러한 CBR의 여러 설계요소들 중, '결합할 유사사례의 선택' 과 관련해, CBR이 보다 개선된 형태로 경영문제 해결에 응용될 수 있는 모형을 제시하고 있다. 본 연구의 제안모형은 결합할 유사사례를 선택하는 기준으로 특정 사례수(k-NN)나 유사도의 상대적 비율을 사용하는 기존의 CBR과 달리 0에서 1사이의 값을 갖는 절대적 유사 임계치를 적용하고 있다. 다만, 절대적 유사 임계치를 사용할 때, 그 값이 작아질 경우 예측결과의 생성이 과도하게 이루어지지 않을 수 있는 문제를 해결하기 위해, 커버리지를 모형에 함께 반영하여 사용자가 원하는 수준의 커버리지는 유지한 상태에서 가장 효과적인 유사 사례를 찾아, 추론을 수행할 수 있도록 설계하였다. 제안모형을 검증하기 위해, 본 연구에서는 이 모형을 실제 인터넷 쇼핑몰의 고객 발굴 사례에 적용해 보았다. 이를 통해, 제안모형의 적용가능성을 확인하고, 향후 추가연구가 요구되는 개선방향을 고찰해 보았다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.535-542
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• 2013

Since case-based reasoning(CBR) has many advantages, it has been used for supporting decision making in various areas including medical checkup, production planning, customer classification, and so on. However, there are several factors to be set by heuristics when designing effective CBR systems. Among these factors, this study addresses the issue of selecting appropriate neighbors in case retrieval step. As the criterion for selecting appropriate neighbors, conventional studies have used the preset number of neighbors to combine(i.e. k of k-nearest neighbor), or the relative portion of the maximum similarity. However, this study proposes to use the absolute similarity threshold varying from 0 to 1, as the criterion for selecting appropriate neighbors to combine. In this case, too small similarity threshold value may make the model rarely produce the solution. To avoid this, we propose to adopt the coverage, which implies the ratio of the cases in which solutions are produced over the total number of the training cases, and to set it as the constraint when optimizing the similarity threshold. To validate the usefulness of the proposed model, we applied it to a real-world target marketing case of an online shopping mall in Korea. As a result, we found that the proposed model might significantly improve the performance of CBR.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.163-170
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• 2024

Red ginseng is manufactured as a health-functional food and is also present in various food types and in different product forms. However, there is currently no standardized regulation of ginsenoside content in foods containing red ginseng. In the present study, we analyzed the ginsenoside content of 66 red ginseng-containing foods and 35 health-functional foods collected online and directly from the market. The ginsenoside content was assessed using liquid chromatography (LC) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods. The ginsenoside content of the various food types ranged 0.0 (not detected)-71.567 mg per daily intake of foods containing red ginseng. Sugar-preserved foods had the highest ginsenoside content, followed by solid teas, liquid teas, and red ginseng beverages. For health-functional foods, the ginsenoside content ranged 3.4-58.5 mg per daily intake, with levels ranging 83-607% of the indicated amounts. All values met the established standards. Upon comparing red ginseng health-functional foods and red ginseng-containing foods, the average ginsenoside content was determined to be 18.21 and 8.79 mg, respectively, thus being nearly twice as high in health-functional foods. However, there was a minimal difference between the ginsenoside content of red and black ginseng, with values of 11.84 and 12.63 mg, respectively. These findings provide insights on the variations in ginsenoside content of red and black ginseng in various food forms. This information is expected to be valuable for future regulations and consumer choice of products containing red ginseng.