• Archives of design research
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• v.20
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• pp.111-120
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• 1997

Recently, rapidly increasing internet Websites are providing us with the new kinds of multimedia informations without borders acting as the center for exchanging informations. Such new media informations through the internet passes informations via light on the monitor and provides the various informations, with the differentiation from the traditional printing media, it can be searched with electronic commands in limited space. In the process of adapting the new technologies, new media has successfully responded to the fast change and the development of its needs by experiencing the trials and errors, steadily establishing the stable position with its new information transferring and exchanging methods. The representative hompage of websites of information transformations means the first page containing no lower directories and consist of titles, icons, symbols and addresses and can design them in consideration of graphical process, planning, contents and others. Such hompages are very important since the graphical images shows its visual expressions deciding the total meanings of the hompages. In this research, we have analyzed the visual factors of frequencies, ratio of areas, distributions, alignment methodologies on layouts of hompages consisting titles, icons, contents and symbols, etc. from randomly picked samples of 161 hompages of websites in the internets of various areas. Generally, the homepages are designed with graphical expressions in personal way and the feedbacks and responses of such may differs, but we think, this can be used as reference materials for the analysis of new media in objective way. Also, it can be used as the base informations for arrangement and planning of designs with the characteristics of graphics and Graphic User Interfaces in the background which are implemented over internet.

• Information Systems Review
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• v.22 no.1
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• pp.59-72
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• 2020

Artificial Intelligence is establishing itself as a familiar tool from an intractable concept. In this trend, financial sector is also looking to improve the problem of existing system which includes Fraud Detection System (FDS). It is being difficult to detect sophisticated cyber financial fraud using original rule-based FDS. This is because diversification of payment environment and increasing number of electronic financial transactions has been emerged. In order to overcome present FDS, this paper suggests 3 types of artificial intelligence models, Generative Adversarial Network (GAN), Deep Neural Network (DNN), and Convolutional Neural Network (CNN). GAN proves how data imbalance problem can be developed while DNN and CNN show how abnormal financial trading patterns can be precisely detected. In conclusion, among the experiments on this paper, WGAN has the highest improvement effects on data imbalance problem. DNN model reflects more effects on fraud classification comparatively.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.117-120
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• 2005

인구주택총조사는 국가통계의 가장 기본이 되는 자료를 생산하는 조사로 거의 대부분의 나라에서 전수조사방식으로 정기적으로 시행해왔다. 그러나 최근 들어 일부 국가, 특히 선진국에서 응답거부가 늘고, 조사대상을 접촉하기 어려운 등 조사환경이 나빠지고 있다. 아울러 조사비용이 급격하게 증가하고 있다. 이에 각 국의 통계청에서는 이러한 상황을 인구센서스에 대한 '근본적인 도전'으로 간주하고 있다(Jensen, 2000). 심지어 독일이나 네델란드에서는 조사환경의 악화로 1990년대 이후 인구센서스를 중단한 상태이다(Bierau, 2000). 조사환경의 악화는 조사의 포괄성과 신뢰성에 대한 문제를 야기한다. 선진국들과 마찬가지로 우리나라에서도 조사환경이 빠른 속도로 악화되고 있다. 더욱이 우리의 경우 읍면동사무소 기능축소로 말미암아 과거 인구주택총조사에서 실제 조사에 도움을 주었던 행정지원이 없어짐에 따라 앞으로 조사의 어려움은 더욱 커질 것으로 보인다. 이렇듯 악화되는 조사환경변화에 대응하여 선진 국가에서는 다양한 형태의 인구센서스방식들이 모색되고 있다. 많은 나라들이 순환형 센서스보다는 행정자료를 인구주택총조사에 활용하는 방안을 모색하고 있으며, 덴마크나 핀란드 등 일부 국가에서는 이미 전혀 조사를 하지 않고 행정자료로 대부분의 인구센서스 통계를 생산하고 있다(Harala, 1996; Gaasemyr, 1999; Laihonen, 1999), 많은 나라들이 행정자료를 활용한 인구센서스 방식을 선호하는 데는 또 다른 이유가 있다. 자료의 측면에서 보면, 행정자료를 활용할 경우 매년 인구센서스 통계를 생산할 수 있다. 실제로 현재 덴마크와 핀란드는 인구센서스에 준하는 통계를 매년 생산하고 있다. 또한 이러한 자료를 바탕으로 지역통계 수요에 즉각 대처할 수 있다. 더 나아가 이와 같은 통계는 전 국민에 대한 패널자료이기 때문에 통계적 활용의 범위가 방대하다. 특히 개인, 가구, 사업체 등 사회 활동의 주체들이 어떻게 변화하는지를 추적할 수 있는 자료를 생산함으로써 다양한 인과적 통계분석을 할 수 있다. 행정자료를 활용한 인구센서스의 이러한 특징은 국가의 교육정책, 노동정책, 복지정책 등 다양한 정책을 정확한 자료를 근거로 수립할 수 있는 기반을 제공한다(Gaasemyr, 1999). 이와 더불어 행정자료 기반의 인구센서스는 비용이 적게 드는 장점이 있다. 예를 들어 덴마크나 핀란드에서는 조사로 자료를 생산하던 때의 1/20 정도 비용으로 행정자료로 인구센서스의 모든 자료를 생산하고 있다. 특히, 최근 모든 행정자료들이 정보통신기술에 의해 데이터베이스 형태로 바뀌고, 인터넷을 근간으로 한 컴퓨터네트워크가 발달함에 따라 각 부처별로 행정을 위해 축적한 자료를 정보통신기술로 연계${cdot}$통합하면 막대한 조사비용을 들이지 않더라도 인구센서스자료를 적은 비용으로 생산할 수 있는 근간이 마련되었다. 이렇듯 행정자료 기반의 인구센서스가 많은 장점을 가졌지만, 그렇다고 모든 국가가 당장 행정자료로 인구센서스를 대체할 수 있는 것은 아니다. 행정자료로 인구센서스통계를 생산하기 위해서는 각 행정부서별로 사용하는 행정자료들을 연계${cdot}$통합할 수 있도록 국가사회전반에 걸쳐 행정 체제가 갖추어져야 하기 때문이다. 특히 모든 국민 개개인에 관한 기본정보, 개인들이 거주하며 생활하는 단위인 개별 주거단위에 관한 정보가 행정부에 등록되어 있고, 잘 정비되어 있어야 하며, 정보의 형태 또한 서로 연계가 가능하도록 표준화되어있어야 한다. 이와 더불어, 현재 인구센서스에서 표본조사를 통해 부가적으로 생산하는 경제활동통계를 생산하기 위해서는 개인이 속한 사업체를 파악할 수 있도록 모든 사업체가 등록되어 있고, 개인의 경제활동과 관련된 각종 정보들이 사업체에 잘 기록 및 정비되어 있어야 한다. 따라서 행정자료 기반의 인구센서스통계생산은 단지 국가의 통계뿐만 아니라 행정조직과 행정체계를 정비하고, 개인과 사업체의 등록체계를 정비하며, 사업체의 개인에 관한 정보를 정비하여 표준화하는 막대한 작업을 수반한다. 이런 이유에서 대부분의 국가들은 장래에 행정자료 기반의 인구센서스통계생산을 목표로 하되, 당장은 행정자료를 인구센서스에 보조적 수단을 사용하는 데 노력을 기울이고 있다. 우리나라의 경우 행정자료를 인구주택총조사에 활용할 수 있는 몇 가지 중요한 기반을 갖추고 있다. 첫째, 1962년부터 시행한 주민등록제도가 있다. 주민등록제도는 모든 국민 개개인을 파악할 수 있는 주민등록번호를 갖추고 있으며 40년 이상 제도화되어 오류가 거의 없는 편이다. 둘째, 세계 10위권 내에 들 정도로 높은 우리나라의 정보화 수준과 2000년부터 시작된 전자정부사업으로 행정자료를 연계${cdot}$통합할 수 있는 기반이 잘 갖추어져 있다. 반면, 우리나라 행정자료 가운데 주거(생활)단위와 사업체를 파악할 수 있는 자료는 매우불완전하다. 대표적으로 인구센서스통계의 주요한 단위인 가구를 파악할 수 있는 수준으로 주소체계가 정비되어 있지 않으며, 많은 사업체, 특히 소규모 사업 가운데 등록되어 있지 않거나 등록오류가 많은 편이다. 이외에도 과세대장, 토지대장 등 많은 행정자료가 아직은 불완전하여 이들을 직접 연계하기에 어렵다. 행정자료를 연계하기 위해서는 모든 자료를 정비하고 표준화하여 실제 행정에 활용하여야 하기 때문에 행정적으로 많은 노력과 시간이필요하다. 따라서 현재는 손쉬운 부분에서부터 인구주택총조사에 행정자료를 활용하고, 앞으로 활용 과정을 거치면서 행정자료를 정비하고 표준화하는 장기적인 방안을 마련할 필요가 있다.

• 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.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.