• 한국IT서비스학회:학술대회논문집
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• 2008.11a
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• pp.112-115
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• 2008

CRM is one of fields that come into the spotlight in academic circles. A Marked changes of business environment makes it get a various information about competitive products unlike in the past and makes it understand the customer needs. Also, Market boundary become to be uncleared. Insurance industry is lied in the age of limitless competition due to uncleared market boundary. Channels for getting customer information and understandings become to be various. In this study, we collect the customer information using various channel and we analyze out a primary factor. Using this results, we present the method that cluster target customers. It is the object of this paper that analyze out the effects when we execute the One-to-One-Marketing using clustered target customer based customer pattern.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.457-465
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• 2002

휴대폰 사용 인구가 폭발적으로 늘어나 대다수 사람을 네트워크화 할 수 있는 기반 환경이 조성되었다. 이에 따라 모바일 단말을 통한 여러 응용 서비스가 가능해 졌다. 따라서 본 논문에서는 휴대폰의 SMS(Short Message Service) 기능을 이용한 Callback URL을 적용하여 구축한 예약 확인 시스템을 통해 예약이 필요한 기업 및 기관에서 예약 손실을 줄이고 예약 데이터를 이용하여 eCRM과 연계, 고객 특성을 관리하고 마케팅 활동에 활용할 수 있는 방안을 다루고자 한다.

• Information Systems Review
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• v.5 no.1
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• pp.1-18
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• 2003

Many organizations experience that the value they gained from IT (information technology) investment is lower than they expected prior to the actual implementation of a system - the value gap between expected value and realized value from IT. Research on the barriers to the expectation and realization is of high importance both in practice and in IS research. This study analyzed such barriers observed in three companies that have adopted such IT systems as Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Customer Relationship Management (CRM) systems each. From analyses of the three companies we specifically identified eight types of barriers: barriers related to industry, organization, knowledge, resource, usage, competence, product, and safety. Common barriers among the three cases were classified into six types, e.g. lack of strategic mind-set, improper organizational structure, lack of human resource, etc. We expect that the results help managers in IS investment to minimize the valuation gap and maximize realized values.

• The Journal of the Korea Contents Association
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• v.2 no.1
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• pp.113-119
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• 2002

E-Biz. system must provide convenient and easy interface, efficient functions, and provides information and contents satisfying customers. To do this, many kinds of studies are being advanced actively about E-Biz. system using intelligent agent technology and efficient E-Biz. strategy. Also E-Biz. system score a lot of information and contents to get a efficient business activities and relationships between a company arid customers. To effectively adopt individual customer's preference and actively adapt change of business situation, we may consider individual user characteristics and emotion. This paper suggests E-Biz. system using intelligent agent technology based on user characteristics and emotion. This system can provide convenient user interface and improve customer royalty. In the results, it is found that proposed system is more efficient than existing systems about customer's preference.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.2165-2168
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• 2003

인터넷을 기반으로 기업 활동이 점차로 넘어지면서, 동적이면서 실시간의 데이터를 제공하기 위하여 거미줄처럼 여러 개의 시스템이 연계되기도 하고, ERP, CRM, EAI등으로 기업 내 Application을 통합하기도 한다. Backend System의 통합으로 얻을 수 있는 기대효과에 맞서서 이의 인터넷 서비스 위험은 더 커져 정보 보안 문제가 중요한 이슈가 되었다. 이에 본 논문에서는 웹 기반 사용자의 정보를 안전하게 보호하고, 여러 가지 보안 문제를 해결할 수 있는 안전한 애플리케이션 보안 구조에 대한 설계 및 구현에 대해 기술하였다. 기업의 웹 서비스는 B2B 와 B2E 로 구분되는 데, 특히 B2B 거래 시의 법적 증명력을 가지기 위한 전자 거래 법 규정에 따른 정보 시스템의 보안 구성 요건을 철저히 분석하여, 시스템 구현에 적용하였다. 아울러 향후 기업의 웹 기반 애플리케이션 시스템 보안 구조의 발전방향을 살펴보면서 웹 기반 정보 시스템의 보안 환경을 검토하거나, 추진할 때 도움이 될 수 있고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.225-234
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• 2007

This research is based on the purpose of awaking the importance to publicity of terrorism prevention in the environment of neo-terrorism and presenting the direction of effective publicity activities of terrorism prevention in the counter-terrorism system. Government publicity through public media plays a significant role in promoting people's participation and improving the awareness. So, to strengthen the terrorism prevention in environmental changes of terror occurrence, active method available for people must be found as publicity method of terrorism prevention suitable for high information society. For this method, this research argued first about relationship between police organization and public as counter-terrorism system and about effective publicity methods of terrorism prevention through active erection of these relationships. This research suggested the operation method through introduction of e-CRM and etc, with ultimate purpose about maximizing the publicity effect of terrorism prevention by using as the advantage of internet these days as possible. And needs of information service activities and other administration strategies of publicity of terrorism prevention are suggested through enlarging the distribution scope of governmental counter-terrorism information materials by strengthening the national publicity activities and using media.

• Proceedings of the CALSEC Conference
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• 2003.09a
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• pp.78-83
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• 2003

본 연구에서는 건설CALS/EC 기반의 정보화 추진을 위한 정보시스템을 개발할 때 적용되는 기존의 소프트웨어 개발방법론의 문제점을 분석하고, 표준화되지 않은 개발방법론의 적용으로 시스템 개발에 비효율적인 비용을 투자하는 문제점과 특성상 입찰, 구매, 계약 등 분야별로 상이한 기능의 업무에 따라 구축되는 건설산업 정보화에 있어서 상호간에 데이터 및 프로세스를 원활히 통합하지 못하므로 인하여 발생되는 개발의 중복성, 정보 활용의 비효율성 등의 문제점을 개선하고자 하였다. 이를 위하여 최근 새로이 부각되고 있는 방법론인 컴포넌트 기반 개발방법론(CBD: Component-Based Development)을 사용하여 건선CALS/EC 표준지침 웹서비스를 위한 시스템의 업무프로세스를 모델링하여 사용자가 쉽게 재사용가능하고 타업무분야에 확대 가능한 방향을 제시하였다. 본 연구를 위하여 기존의 소프트웨어 개발 방법론의 적용상 문제점 분석을 통하여 컴포넌트 기반 개발방법론의 필요성을 증명하고 비즈니스 컴포넌트 프레임워크를 사용하여 건설 CALS/EC 표준지침 웹서비스의 업무프로세스에 적용할 기술적인 방법론을 고찰하고 업무에 효과적으로 적용할 수 있는 업무프로세스 컴포넌트를 제시하였다. 본 연구에서 도출된 컴포넌트 모델은 향후 타업무분야의 시스템을 개발할 때 사용자요구분석 단계부터 별도의 재 작업이 없이 사용될 수 있으며 시스템을 구현할 때 개발 모듈의 중복방지와 용이한 비즈니스로직의 변경 등이 가능하며, 추가의 업무 프로세스나 연관된 다른 분야의 업무프로세스의 반영 및 추가 시 컴포넌트의 활동모델을 쉽게 수정하여 정의함으로써 쉽게 시스템의 기능을 확장할 수 있다.LE 산정에 관한 지속적인 실험적 연구가 이루어져야 하겠다. 증가할 것이다. 또한 부분육을 이용한 완전제품, 적색육제품, 유기농이나 별미식 제품과 같은 형태의 다양한 포장육 제품이 도입 될 것으로 생각되어진다.e in vitro SPF test method will be able to be used as an alternative method for in vivo SPF in case of lotion and cream. replica. A statistically significant improvement of Star Fruit Leaf Extract BG30-treated site was seen in decreased wrinkles. Star Fruit Leaf Extract BG30 results in clinically visible improvement in wrinkling when used topically for 5 weeks. 또한 관계마케팅, CRM 등의 이론적 배경이 되고 있는 신뢰와 결속의 중요성이 재확인하는 결과도 의의라고 할 수 있다. 그리고 신뢰는 양사 간의 상호관계에서 조성될 수 있는 특성을 가진 반면, 결속은 계약관계 초기단계에서 성문화하고 규정화 할 수 있는 변수의 성격이 강하다고 할 수가 있다. 본 연구는 복잡한 기업간 관계를 지나치게 협력적 측면에서만 규명했기 때문에 많은 측면을 간과할 가능성이 있다. 또한 방법론적으로 일방향의 시각만을 고려했고, 횡단적 조사를 통하고 국내의 한 서비스제공업체와 관련이 있는 컨텐츠 공급파트너만의 시각을 검증했기 때문에 해석에서 유의할 필요가 있다. 또한 타당성확보 노력을 기하였지만 측정도구

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used