• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.99-101
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• 1998

점차 규모가 확대되어 가는 전자상거래 분야에서 차별화를 위하여 개인화된 서비스 제공을 위한 one-to-one 마케팅을 도입하였다. 기존의 가상 상점들을 사용자가 구매를 원하는 물건을 검색하여 찾아야 하는데, push기술과 에이젼트를 도입함으로써 고객에게 맞춤 서비스를 제공한다. 고객의 선호도 조사와 로그인한후의 행위를 모니터링하고 구매결과들을 분석하여 다음 고객의 방문시 그 고객만의 상점을 제공해준다. 모니터링 에이젼트(Monitoring Agent), 분석 에이젼트(Analysis Agent)의 도입으로 고객이 정말 원하는 것이 무엇인지 고객이 인지 못하고 있는 것도 제시함으로써 정보의 유용성을 높여주고, 개인화 에이젼트(Personalize Agent)를 통해 고객의 편의 도모뿐만 아니라 Rule-based에 근거하여 고객이 관심 있을 만한 상품과 광고를 선정해 제시함으로써 구매를 자극한다. 본 시스템에선 자바의 "Write Once, Run Anywhere" 성격에서 잘 나타난 분산환경에서의 장점을 이용하고자 최대한 자바의 기술을 사용해서 시스템을 설계하고 에이젼트를 만들었다.이젼트를 만들었다.

• Electronics and Telecommunications Trends
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• v.30 no.3
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• pp.52-63
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• 2015

디지털 기술의 발달과 함께 인터넷의 보급 확산은 다양한 미디어 소비 채널을 양산하였고, 정보취득 및 소비 형태의 다양화를 유도하였다. 최근 스마트폰, 태블릿PC 등 모바일 기기가 확산되고 스마트TV, 디지털사이니지와 같은 새로운 매체가 등장함에 따라 이들을 통해서 제공되는 광고가 시장에서 주목을 받고 있다. 또한 양방향 매체의 확산에 힘입어 광고 소비 매체의 특성을 활용한 다양한 맞춤형광고, 리워드광고, 네이티브광고, 커머스 연계 광고 등 새로운 유형의 광고 상품 및 서비스들이 제공되고 있으며 광고주들의 관심을 끌고 있다. 새로운 스마트미디어를 이용한 광고산업 활성화 및 경쟁력 확보를 위해서는 차세대 광고플랫폼, 다양한 광고 효과 측정 및 분석기술 등에 대한 개발 및 기반구축이 선행되어야 한다. 본고에서는 주요 매체별 광고 유형에 대하여 살펴보고 다양한 스마트미디어 광고 서비스 유형 및 사례, 광고효과 측정관련 이슈 및 기술동향 등에 대하여 살펴본다.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• 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 Internet Computing and Services
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• v.24 no.6
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• pp.41-47
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• 2023

Financial instruments valuation continues to evolve due to various technological changes. Recently, there has been increased interest in valuation using machine learning and artificial intelligence, enabling the financial market to swiftly adapt to changes. This technological advancement caters to the demand for real-time data processing and facilitates accurate and effective valuation, considering the diverse nature of the financial market. Numerical analysis techniques serve as crucial decision-making tools among financial institutions and investors, acknowledged as essential for performance prediction and risk management in investments. This paper analyzes Korean patent trends of numerical analysis-based financial systems, considering the diverse shifts in the financial market and asset data to provide accurate predictions. This study could shed light on the advancement of financial technology and serves as a gauge for technological standards within the financial market.

• Journal of Industrial Convergence
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• v.22 no.2
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• pp.45-54
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• 2024

This paper attempted to analyze the concept of the New Silver Generation and the New Silver Generation's perception and consumption structure of jewelry products. A survey was conducted focusing on those interested in jewelry, and through this, we sought to find a direction for future marketing plans and identify the characteristics and consumption patterns of the new silver generation and the types of jewelry they prefer. Through the survey results, we sought results on jewelry marketing plans and methods for the new silver generation, and as a result, we were able to suggest a marketing direction for the new silver generation. There is a need for research on the development of design products for the future jewelry market based on our response to the future silver industry and marketing directions to generate profits in the business area.

• Science of Emotion and Sensibility
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• v.19 no.1
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• pp.39-48
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• 2016

This research aims to identify innovative companies' attention to the emotion and sensibility and its application by analyzing the contents of the corporate annual reports. Annual report is a good reference data because it describes not only various current products and services' annual activities and business performance but also corporate future direction. Sensibility is interpreted and used with various words internationally: various related terms such as sensibility, sense, emotion, feeling and affection are analyzed not only by the definition but also the interrelationship among them, and included for the contents analysis. To select the innovative companies, the researcher used 'Fast company' that is the economic journal and deducted the companies list via 'The world's 50 most innovative companies' in 2009 and 2014. Listed companies' 2009 and 2014 annual reports' contents were analyzed to identify the rate of the recognition and the application of sensibility to their business. Even though the quantitative result of the content analysis indicates not a strong relationship between corporate innovativeness and 'sensibility' qualitative result identifies companies are paying more attention to the 'sense' and 'feeling' during five years. In conclusion, the innovation that company pursues strategically is shifting from differentiation and the technological leadership to satisfying user experiences and the number of companies which express and measure user feeling and emotions are increasing.

• Culinary science and hospitality research
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• v.18 no.5
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• pp.129-145
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• 2012

This paper investigated the awareness and activation of food tourism festivals in Gwangju, Jeonnam Province. A survey was conducted for the visitors of the Gwangju Kimchi Festival at nearby Gwangju Jeungoe park from October 15 to 19, 2011, and finally 207 respondents were analyzed. As a results of this study, the visitors who visited the Gwangju Kimchi Festival have a lot of interest in local food and food festivals. Also, most of the visitors have much more affection for the area and the food culture developed by geographical influence. The Gwangju Kimchi festival proved to be the most popular food tourism festival in Gwangju, Jeonnam among others. There are much more food festivals than other regions in Gwangju, Jeonnam because of popularity of food festivals, a variety of food, and various kinds of food ingredients. What is necessary to activate the food tourism festivals in Gwangju, Jeonnam includes a variety of programs related to food tourism experience, the development of competitive food tourism products, and PR for well-being food of these areas.

• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.105-113
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• 2015

This paper investigated Korean popular entertainment (K-entertainment, hereafter) tourism using theory of intention-behavior. Dividing the sample of international visitors to Seoul into the groups of "inclined actors", "inclined abstainers", and "disinclined abstainers," we examined the factors affecting such a division. The results showed that between the inclined actors and the inclined abstainers, there was no significant difference in the years of exposure to K-entertainment contents or the frequency of going to K-entertainment performance in the past. In contrast, the inclined actors were found to have a greater level of knowledge, ability, and cooperation compared to the inclined abstainers. In addition, we found that the K-entertainment performance in Korea was perceived as a substitute, rather than a complement to the performance held elsewhere in the world. Our findings suggest that interests in K-entertainment alone are not sufficient to drive the international visitors' intention to the action of attending K-entertainment performance during their visit to Korea. To this end, this study implies that it is necessary to design attractive tourism packages that include K-entertainment performance.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.345-352
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• 2002

무선 인터넷을 이용하는 사용자는 정보의 양의 따른 시간적 통신비용의 증가 문제로 개인화 에이전트가 사용자의 관심에 따라 서비스를 제공하는 기능과 맞춤화된 정보를 제공하는 기능, 지식 기반 방식으로 정보를 예측하는 기능을 가지기를 바라고 있다. 본 논문에서는 이와 같이 무선 인터넷을 사용하는 사용자를 위한 PDA 개인화 에이전트 시스템을 구축하고자 한다. PDA 개인화 에이전트 시스템 구축을 위해 프로파일 기반의 에이전트 엔진과 사용자 프로파일을 이용한 지식기반 방식을 사용한다. 사용자가 웹페이지에서 행하는 행위들을 모니터링하여 사용자가 관심 가지는 문서를 파악하고 정보 검색을 통해 얻어진 문서를 분석하여 사용자 각각의 관심 문서로 나누어 서비스하게 된다. 모니터링 되어진 문서를 효과적으로 분석하기 위해 unsupervised clustering 기계학습 방식인 Cobweb을 이용한다. unsupervised 기계 학습은 conceptual 방식을 이용하여 검색되어진 정보를 사용자의 관심 분야별로 clustering한다. 클러스터링을 통해 얻어진 결과를 다시 기계학습을 통해 사용자 관심문서에 대한 프로파일을 생성하게 된다. 이렇게 만들어진 프로파일을 룰(Rule)로 만들어 이를 기반으로 사용자에게 서비스하게 된다. 이러한 룰은 사용자의 모니터링 결과로 얻어지기 때문에 주기적으로 업데이트하게 된다. 제안하는 시스템은 인터넷신문이나 웹진 등에서 사용자들에게 뉴스를 전달하기 위한 목적으로 생성하는 뉴스문서를 특정 대상으로 선정하였고 사용자 정보를 이용한 검색을 실시하고 결과로 얻어진 정보를 정보 분류를 통해 PDA나 휴대폰을 통해 사용자에게 제공한다. 상품을 검색하기 위한 검색노력을 줄이고, 검색된 대안들로부터 구매자와 시스템이 웹상에서 서로 상호작용(interactivity) 하여 해를 찾고, 제약조건과 규칙들에 의해 적합한 해를 찾아가는 방법을 제시한다. 본 논문은 구성기반 예로서 컴퓨터 부품조립을 사용해서 Template-based reasoning 예를 보인다 본 방법론은 검색노력을 줄이고, 검색에 있어 Feasibility와 Admissibility를 보장한다.매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of compu