• The Journal of Korean Society for School & Community Health Education
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• v.1 no.1
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• pp.11-43
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• 2000

The purpose of this study was, through analyzing the previous researches, to grasp the present status of environment of school building(ESB), research the sundry records of each element and, through comparative analysis of the standard of ESB in Korea, the United States, and Japan, select the normative standard of ESB, to clarify the point at issue presented in Regulation of Construction & facility Management for Elementary and and Secondary School in Korea, and to suggest an alternative preliminary standard of ESB. To carry out a research for this purpose, these were required: 1. to investigate the existing present status of ESB, 2. to make a comparative analysis of the standard of ESB in each country, 3. to suggest the normative standard of preliminary standard of ESB, 4. to analyze the controversial points of the standard of ESB in Korea, 5. to suggest an alternative preliminary standard of ESB. The conclusions were as follows: 1. Putting, through analyzing the previous researches, the existing present status of ESB together, it seemed that lighting environment, indoor air environment and noise environment were all in poor conditions. 2. In the result of a comparative analysis of the standard of ESB in Korea, Japan and the United States, in Korea the factors of each lighting and indoor air environment were not presented properly, in Japan, in lighting environment aspect, the standard on natural lighting and the factors on brightness were not presented., and in the USA the essential factors of each environment were throughly presented. In the comparison of the standards on each factor, Korea showed that the standard level presented was less properly prescribed than those of the USA and Japan but it also showed that the standard levels prescribed in the USA and in Japan were mostly similar to the standard levels in records investigated. 3. With the result of the normative standard selection on School Builiding environment factor of prescribed in this study, the controversial points of the standard of ESB in Korea were analyzed and the result was utilized to suggest new preliminary standard of ESB. 4. As the result of the analysis of the controversial points of the standard of ESB in Korea, it was found that the standard of ESB in Korea should be established on a basis of School Health Act and be concretely presented in School Health Regulation and School Health Rule. The factors of each environment was improperly presented in the existing standard of ESB in Korea. Moreover the standard of them was inferior to that of the records investigated and those of in the USA and in Japan and it also showed that the standard of it in Korea was improper to maintain Comfortable Learning Environment. 5. A suggested preliminary standard of ESB acquired through above study as follows: 1) In this study a new kind of preliminary standard of ESB is divided into lighting environment, indoor air environment, noise environment, odor environment and for above classification, reasonable factor and standard should be established and the controling way on each standard and countermeasures against it should be considered. 2) In lighting environment, the factors of natural lighting are divided into daylight rate, brightness, glare. In the standard on each factor, daylight rate should secure 5% of a mean daylight rate and 2% of a minimum daylight rate, brightness ratio of maximum illumination to minimum illumination should be under 10:1, and in glare there should not be an occurrence factor from a reflector outside of the classroom. And the factors of unnatural lighting are illumination, brightness, and glare. In the standard on each factor, illumination should be 750 lux or more, brightness ratio should be under 3 to 1, and glare should not occur. And Optimal reflection rate(%) of Colors and Facilities of Classroom which influences lighting environment should be considered. 3) In indoor air environment factors, thermal factors are divided into (1) room temperature, (2) relative humidity, (3) room air movement, (4) radiation heat, and harmful gases (5) CO, (6) $CO_2$ that are proceeded from using the heating fuel such as oval briquettes, firewood, charcoal being used in most of the classroom, and finally (7) dust. In the standard on each factor, the next are necessary; room temperature: $16^{\circ}C{\sim}26^{\circ}C$(summer : $E.T18.9{\sim}23.8^{\circ}C$, winter: $E.T16.7{\sim}21.7^{\circ}C$), relative humidity: $30{\sim}80%$, room air movement: under 0.5m/sec, radiation heat: under $5^{\circ}C$ gap between dry-bulb temperature and wet-bulb temperature, below 1000 ppm of ca and below 10ppm of $CO_2$, dust: below 0.10 $mg/m^3$ of Volume of dust in indoor air, and ventilation standard($CO_2$) for purification of indoor air : once/6 min.(about 7 times/40 min.) in an airtight classroom. 4) In the standard on noise environment, noise level should be under 40 dB(A) and the noise measuring way and the countermeasures against it should be considered. 5) In the standard on odor environment, odor level under Physical Method should be under 2 degrees, and the inspecting way and the countermeasures against it should be considered.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Journal of Korea Technology Innovation Society
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• v.20 no.2
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• pp.334-367
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• 2017

The ICT industry has been the main driver of Korea's economy with international competitiveness and is expected to be the growth engine that will revitalize the currently depressed economy. A broad range of different perspectives and opinions on the industry exist in Korea and overseas. Some of these are stereotypes, not all of which are based on objective evidence. Stereotypes refer to widely-held fixed opinions on a specific group and do not necessarily have negative connotations. However, they should not be viewed lightly because they can substantially affect decision-making process. In this regard, this study sought to review the stereotypes of ICT industry and identify objective and relative stereotypes. In the study, a decision-tree analysis was conducted on a survey result of 3,300 small and medium-sized enterprises (SMEs) in order to identify Korean ICT companies' characteristics that distinguish them from other technology companies. The decision-tree analysis, a data mining process based on machine learning, took a total of 291 variables into account in 10 subjects such as: corporate business in general, technology development activities as well as organization and people in technology development. Identifying the variables that distinguish ICT companies from other technology companies with the decision-tree analysis, the study then came up with a list of objective stereotypes of ICT companies. The findings from the stereotypes of Korean ICT companies are as follows. First, the companies are in need of technology policies that help R&D planning and market penetration. Second, policies must better support the companies working to sell new products or explore new business. Third, the companies need policies that support secure protection of development outcomes and proper management of IP rights. Fourth, the administrative procedures related to governmental support for ICT companies' R&D projects must be simplified. It is hoped that the outcome of this study will provide meaningful guidance in establishment, implementation and evaluation of technology policies for ICT SMEs, particularly to policymakers or researchers in relevant government agencies who determine R&D policies for ICT SMEs.

• Journal of dental hygiene science
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• v.6 no.1
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• pp.11-17
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• 2006

The purpose of this study was to help prevent the turnover of competent dental hygienists in a bid to boost the efficiency of personnel management for dental health care workers and provide higher-quality oral health services. After relevant literature and data were reviewed, a survey was conducted on dental hygienists, who worked at dental institutes, for approximately four months from September to December 2004 to identify what affected their turnover. The findings of the study were as below: 1. Regarding turnover experience, 39.7 percent of the dental hygienists investigated had such an experience. As to turnover frequency, those who took up another employment once made up the largest group(28.2%), followed by twice(8.0%) and three times(2.9%). The most dominant turnover reason was working conditions(66.7%), followed by seeking being hired by larger institutes(36.2%), pay(21.7%), relationship with dentists(11.6%) and commuting distance(11.6%). 2. As for their hope for turnover, 82.8 percent hoped to take up another employment, and working conditions were cited as the most common reason(44.4%), followed by pay(33.3%), commuting distance(18.1%), marriage(13.2%), health/use of leisure time(11.8%), and commuting time(10.4%). 3. Concerning preference for future workplace, 38.5 percent, the largest group, wanted to work at public health clinics. As to a preferred term of working as dental hygienists, 50.0 percent, the greatest group, hoped to serve as dental hygienists until they are financially secure. 34.5 percent, the second largest group, intended to keep working until they reach the age limit. In regard to their responsibility for family economy, 47.7 percent, the greatest percentage, shouldered the partial responsibility for that, and 31.6 percent assumed no responsibility. 4. As to their intention to quit working as dental hygienists, 61.5 percent were willing to do that, and marriage(29.0%) was singled out as the most frequent reason, followed by working conditions(27.1%), child birth(22.4%), health/housework(18.7%), pay(15.9%) and learning/use of free time(15.0%).

• Journal of Yeungnam Medical Science
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• v.2 no.1
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• pp.103-111
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• 1985

Childhood epilepsy which has high prevalence rate and inception rate is one of the commonest problem encountered in pediatrician. In contrast with epilepsy of adult, in childhood epilepsy, more variable and varying manifestations are found because the factors of age, growth and development exert their influences in the manifestations and the courses of childhood epilepsy. Moreover epileptic children have associated problems such as physical and mental handicaps, psychologicaldisorders and learning disability. For these reasons pediatrician who deals with epileptic children experiences difficulties in making diagnosis and managing them. In order to improve understanding and management of childhood epilepsy, authors reviewed 103 cases of epileptic patients seen at pediatric department of Yeungnam University Hospital retrospectively. The patients were classified according to the type of epileptic seizure. Suspected causes of epilepsy, associated conditions of epileptic patients, age incidence and the findings of brain CT were reviewed. Large numbers of epileptic patients (61.2%) developed their first seizures under the age of 5. The most frequent type of epileptic seizure was generalized ionic-clonic, tonic, clonic seizure (49.5%), followed by simple partial seizure with secondary generalization (17.5%), simple partial seizure (7.8%), a typical absence (5.8%) and unclassified seizure (5.8%). In 83.5% of patients, we could not find specific cause of it, but in 16.5% of cases, history of neonatal hypoxia (4.9%), meningitis (3.9%), prematurity (1.9%), small for gestational age (1.0%), CO poisoning (1.0%), encephalopathy (1.0%), DPT vaccination (1.0%), cerebrovascular accident (1.0%) and neonatal jaundice (1.0%) were found, 30 cases of patients had associated diseases such as mental retardation, hyperactivity, delayed motor milestones or their combinations. The major abnormal findings of brain CT performed in 42 cases were cortical atrophy, cerebral infarction, hydrocephalus and brain swelling. This review stressed better designed classification of epilepsy is needed and with promotion of medical care, prevention of epilepsy is possible in some cases. Also it is stressed that childhood epilepsy requires multidisplinary therapy and brain CT is helpful in the evaluation of epilepsy with limitation in therapeutic aspects.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.19-28
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• 2020

The Urban Heat Island (UHI) Effect has intensified due to urbanization and heat management at the urban level is treated as an important issue. Green space improvement projects and environmental policies are being implemented as a way to alleviate Urban Heat Islands. Several studies have been conducted to analyze the correlation between urban green areas and heat with linear regression models. However, linear regression models have limitations explaining the correlation between heat and the multitude of variables as heat is a result of a combination of non-linear factors. This study evaluated the Heat Island alleviating effects in Seoul during the summer by using a deep neural network model methodology, which has strengths in areas where it is difficult to analyze data with existing statistical analysis methods due to variable factors and a large amount of data. Wide-area data was acquired using Landsat 8. Seoul was divided into a grid (30m × 30m) and the heat island reduction variables were enter in each grid space to create a data structure that is needed for the construction of a deep neural network using ArcGIS 10.7 and Python3.7 with Keras. This deep neural network was used to analyze the correlation between land surface temperature and the variables. We confirmed that the deep neural network model has high explanatory accuracy. It was found that the cooling effect by NDVI was the greatest, and cooling effects due to the park size and green space proximity were also shown. Previous studies showed that the cooling effects related to park size was 2℃-3℃, and the proximity effect was found to lower the temperature 0.3℃-2.3℃. There is a possibility of overestimation of the results of previous studies. The results of this study can provide objective information for the justification and more effective formation of new urban green areas to alleviate the Urban Heat Island phenomenon in the future.

• 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.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Korean Journal of Heritage: History & Science
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• v.46 no.2
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• pp.46-69
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• 2013

The results of study on the meaning landscape and environmental design techniques of the Byulup, Yoohoedang garden(Hageowon) based on the story in the collection of Kwon Yi-jin (Yoohoedangjip, 有懷堂集), are as below. First, Yoohoedang Kwon Yi-jin (有懷堂 權以鎭 : 1668~1734) constructed a Byulup garden consisting of ancestor grave, Byulup, garden, and a school, through 3 steps for 20 years in the back hill area of Moosoo-dong village, south of Mountain Bomun in Daejeon. In other words, he built the Byulup(別業, Yoohoedang) by placing his father's grave in the back hill of the village, and then constructed Yoegeongam(餘慶菴) and Geoupjae(居業齋) for protection of the pond(Napoji, 納汚池), garden(Banhwanwon, 盤桓園), and ancestor graves, and descendants' studying in the middle stage. He built an extension in Yoohoedang and finally completed the large-size garden (Hageowon) by extending the east area. Second, in terms of geomancy sense, Yoohoedang Byulup located in Moosoo-dong village area is the representative example including all space elements such as main living house (the head family house of Andong Kwon family), Byulup (Yoohoedang), ancestor graves, Hagoewon (garden) and Yoegeongam (cemetery management and school) which byulup type Byulseo should be equipped with. Thirdly, there are various meaning landscape elements combining the value system of Confucianism, Buddhism and Taoism value, including; (1) remembering parents, (2) harmonious family, (3) integrity, (4) virtue, (5) noble personality, (6) good luck, (7) hermit life, (8) family prosperity and learning development, (9) grace from ancestors, (10) fairyland, (11) guarding ancestor graves, and (12) living ever-young. Fourth, after he arranged ancestor graveyard in the back of the village, he used surrounding natural landscapes to construct Hagoewon garden with water garden consisting of 4 mountain streams and 3 ponds for 13 years, and finally completed a beautiful fairyland with 5 platforms, 3 bamboo forests, as well as the Seokgasan(石假山, artificial hill). Fifth, he adopted landscape plantation (28 kinds; pine, maple, royal azalea, azalea, persimmon tree, bamboo, willow, pomegranate tree, rose, chinensis, chaenomeles speciosa, Japanese azalea, peach tree, lotus, chrysanthemum, peony, and Paeonia suffruticosa, etc.) to apply romance from poetic affection, symbol and ideal from personification, as well as plantation plan considering seasonal landscapes. Landscape rocks were used by intact use of natural rocks, connecting with water elements, garden ornament method using Seokyeonji and flower steps, and mountain Seokga method showing the essence of landscape meanings. In addition, waterscape are characterized by active use of water considering natural streams and physio-graphic condition (eastern valley), ecological corridor role that rhythmically connects each space of the garden and waterways following routes, landscape meaning introduction connecting 'gaining knowledge by the study of things' values including Hwalsoodam(活水潭, pond), Mongjeong(蒙井, spring), Hosoo(濠水, stream), and Boksoo(?水, stream), and sensuous experience space construction with auditory and visualization using properties of landscape matters.

• 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.