• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.83-99
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• 2007

Nowadays, the advancement of digital information technology resulting in the increased interest of the management and the use of information has given stimulus to the research on the use and management of information. In this paper, we propose an integrated data mining model that can provide the necessary information and interface to users of scientific information portal service according to their respective classification groups. The integrated model classifies users from log files automatically collected by the web server based on users' behavioral patterns. By classifying the existing users of the web site, which provides information service, and analyzing their patterns, we proposed a web site utilization methodology that provides dynamic interface and user oriented site operating policy. In addition, we believe that our research can provide continuous web site user support, as well as provide information service according to user classification groups.

• Journal of Internet of Things and Convergence
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• v.6 no.2
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• pp.51-56
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• 2020

In this paper, we developed a mobile new media solution that enables e-commerce shopping mall operators, band operators, and YouTube creators to create synergies in online and offline promotion by posting related video contents on the media in addition to their own videos. By providing videos in the field of the platform without directly searching for them, it is possible to provide users with a new type of marketing means that can promote their platform while providing interest and information. Prospective creators at home and abroad who produce video can upload their own video in addition to YouTube and afreeca TV, such as the open market for video, and use independent and free charging systems to manage independent customer relationship management(CRM), self-branding, and content management. It will be possible to utilize mobile-based new media equipped with a system.

• 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.8 no.2
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• pp.139-157
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• 2002

The rapid growth of e-commerce has made both companies and customers face a new situation. Whereas companies have become to be harder to survive due to more and more competitions, the opportunity for customers to choose among more and more products has increased. So, the recommender systems that recommend suitable products to the customer have an important position in E-commerce. This research introduces collaborative filtering based recommender system which helps customers find the products they would like to purchase by producing a list of top-N recommended products. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is used to select target customers, who have high possibility of purchasing recommended products. We applied the recommender system to a Korean department store. The methodology is evaluated with the analysis of a real department store case and is compared with other methodologies.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.92-103
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• 2003

Great many advertisements are serviced in on-line by development of electronic commerce and internet user's rapid increase recently. However, this advertisement service is stopping in one-side service of relevant advertisement rather than doing users' inclination analysis to basis. Therefore, want advertisement service that many websites are personalized for efficient service of relevant advertisement and service through relevant server's log analysis research and enforce. Take advantage of log data of local system that this treatise is not analysis of server log data and analyze user's Preference degree and inclination. Also, try to propose advertisement system personalized by making relevant site tributary category and give weight of relevant tributary. User's preference user preference which analysis is one part of cooperation fielder ring of web personalized techniques use information in visit site tributary and suppose internet user's action in visit number of times of relevant site and try inclination analysis of mixing form. Express user's preference degree by vector, and inclination analysis result uninterrupted data that simplicity application form is not regarded and techniques that propose inclination analysis change of data since with move data use and analyze newly and proposed so that can do continuous renewal and application as feedback Sikkim. Presented method that can choose advertisements of relevant tributary through this result and provide personalized advertisement service by applying process such as user inclination analysis in advertisement chosen.

• (The)Study of the Eastern Classic
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• no.69
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• pp.115-146
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• 2017

This paper is about the research on ontology design for a digital archive of seonsaengan(先生案) of the Joseon Period. Seonsaengan is the register of staff officials at each government office, along with their personal information and records of their transfer from one office to another, in addition to their DOBs, family clan, etc. A total of 176 types of registers are known to be kept at libraries and museums in the country. This paper intends to engage in the ontology design of 47 cases of such registers preserved at the Jangseogak Archives of the Academy of Korean Studies (AKS) with a focus on their content and structure including the names of the relevant government offices and posts assumed by the officials, etc. The work for the ontology design was done with a focus on the officials, the offices they belong to, and records about their transfers kept in the registers. The ontology design categorized relevant resources into classes according to the attributes common to the individuals. Each individual has defined a semantic postposition word that can explicitly express the relationship with other individuals. As for the classes, they were divided into eight categories, i.e. registers, figures, offices, official posts, state examination, records, and concepts. For design of relationships and attributes, terms and phrases such as Dublin Core, Europeana Data Mode, CIDOC-CRM, data model for database of those who passed the exam in the past, which are already designed and used, were referred to. Where terms and phrases designed in existing data models are used, the work used Namespace of the relevant data model. The writer defined the relationships where necessary. The designed ontology shows an exemplary implementation of the Myeongneung seonsaengan(明陵先生案). The work gave consideration to expected effects of information entered when a single registered is expanded to plural registers, along with ways to use it. The ontology design is not one made based on the review of all of the 176 registers. The model needs to be improved each time relevant information is obtained. The aim of such efforts is the systematic arrangement of information contained in the registers. It should be remembered that information arranged in this manner may be rearranged with the aid of databases or archives existing currently or to be built in the future. It is expected that the pieces of information entered through the ontology design will be used as data showing how government offices were operated and what their personnel system was like, along with politics, economy, society, and culture of the Joseon Period, in linkage with databases already established.

• 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