• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.50-63
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• 2018

The rapid urbanization had led to a distortion of natural hydrological cycle system. The change in hydrological cycle structure is causing streamflow depletion, changing the existing use tendency of water resources. To manage such phenomena, a streamflow depletion impact assessment technology to forecast depletion is required. For performing such technology, it is indispensable to build GIS-based spatial data as fundamental data, but there is a shortage of related research. Therefore, this study was conducted to use the use of GIS-based time series spatial data for streamflow depletion assessment. For this study, GIS data over decades of changes on a national scale were constructed, targeting 6 streamflow depletion impact factors (weather, soil depth, forest density, road network, groundwater usage and landuse) and the data were used as the basic data for the operation of continuous hydrologic model. Focusing on these impact factors, the causes for streamflow depletion were analyzed depending on time series. Then, using distributed continuous hydrologic model based DrySAT, annual runoff of each streamflow depletion impact factor was measured and depletion assessment was conducted. As a result, the default value of annual runoff was measured at 977.9mm under the given weather condition without considering other factors. When considering the decrease in soil depth, the increase in forest density, road development, and groundwater usage, along with the change in land use and development, and annual runoff were measured at 1,003.5mm, 942.1mm, 961.9mm, 915.5mm, and 1003.7mm, respectively. The results showed that the major causes of the streaflow depletion were lowered soil depth to decrease the infiltration volume and surface runoff thereby decreasing streamflow; the increased forest density to decrease surface runoff; the increased road network to decrease the sub-surface flow; the increased groundwater use from undiscriminated development to decrease the baseflow; increased impervious areas to increase surface runoff. Also, each standard watershed depending on the grade of depletion was indicated, based on the definition of streamflow depletion and the range of grade. Considering the weather, the decrease in soil depth, the increase in forest density, road development, and groundwater usage, and the change in land use and development, the grade of depletion were 2.1, 2.2, 2.5, 2.3, 2.8, 2.2, respectively. Among the five streamflow depletion impact factors except rainfall condition, the change in groundwater usage showed the biggest influence on depletion, followed by the change in forest density, road construction, land use, and soil depth. In conclusion, it is anticipated that a national streamflow depletion assessment system to be develop in the future would provide customized depletion management and prevention plans based on the system assessment results regarding future data changes of the six streamflow depletion impact factors and the prospect of depletion progress.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Korea journal of population studies
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• v.9 no.1
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• pp.41-53
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• 1986

The purpose of this paper is two-fold. One is to investigate the determinants of the demand supply of labor, and another is to project long-term demand and supply of labor. The paper consists of three parts. In the first part, theoretical models and important hypotheses are discussed: for the case of a labor supply model, issues regarding discouraged worker model, permanent wage hypothesis, and relative wage hypothesis are examined and for the case of a demand model, issues regarding estimating an employment demand equation within the framework of an inverted short-run produc- tion function are inspected. Particularly, a theoretical justification for introducing a demographic cohort variable in a labor supply equation is also investigated. In the second part, empirical results of the estimated supply and demand equations are analyzed. Supply equations are specified differently between primary and secondary labor force. That is, for the case of primary labor force groups including males aged 25 and over, attempts are made to explain the variations in participation behavior within the framework of a neo-classical economics oriented permanent wage hypothesis. On the other hand, for the case of females and young male labor force, variations in participation rates are explained in terms of a relative wage hypothesis. In other words, the participation behavior of primary labor force is related to short-rum business fluctuations, while that of secondary labor force is associated with intermediate swings of business cycles and demographic changes in the age structure of population. Some major findings arc summarized as follows. (1) For the case of males aged 14~19 and 2O~24 groups and females aged 14∼19, the effect of schhool enrollment rate is dominant and thus it plays a key role in explaining the recent declining trend of participation rates of these groups. (2) Except for females aged 20∼24, a demographic cohort variable, which captures the impact of changes in the age structure on participation behavior, turns out to show positive and significant coefficients for secondary labor force groups. (3) A cyclical variable produce significant coefficients for prime-age males and females reflecting that as compared to other groups the labor supply behavior of these groups is more closely related to short-run cyclical variations (4) The wage variable, which represents a labor-leisure trade-off turns out to yield significant coefficients only for older age groups (6O and over) for both males and females. This result reveals that unlike the experiences of other higer-income nations, the participation decision of the labor force of our nation is not highly sensitive with respect to wage changes. (5)The estimated result of the employment demand equation displays that given that the level of GNP remains constant the ability of the economy to absord labor force has been declining;that is, the elasticity of GNP with respect to labor absorption decreasre over time. In the third part, the results of long-term projections (for the period of 1986 and 1995) for age-sex specific participation rates are discussed. The participation rate of total males is anticipated to increase slightly, which is contrary to the recent trend of declining participation rates of this group. For the groups aged 25 and below, the participation rates are forecast to decline although the magnitude of decrease is likely to shrink. On the other hand, the participation rate of prime- age males (25 to 59 years old) is predicted to increase slightly during 1985 and 1990. For the case of females, except for 20∼24 and 25∼34 age groups, the participation rates are projected to decrease: the participation rates of 25∼34 age group is likely to remain at its current level, while the participation rate of 20∼24 age group is expected to increase considerably in the future (specifi- cally, from 55% in 1985 to 61% in 1990 and to 69% in 1995). In conclusion, while the number of an excess supply of labor will increase in absolute magnitude, its size as a ratio of total labor force is not likely to increase. However, the age composition of labor force is predicted to change; that is, the proportion of prime-age male and female labor force is projected to increase.

• 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.

• Knowledge Management Research
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• v.21 no.3
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• pp.45-65
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• 2020

It is desirable to invest in wine that increases its value, but wine investment itself is unfamiliar in Korea. Also, the process itself is unreasonable, and information is often forged, because pricing in the wine market is done by a small number of people. With the right solution, however, the wine market can be a desirable investment destination in that the longer one invests, the higher one can expect. Also, it is expected that the domestic wine consumption market will expand through the steady increase in domestic wine imports. This study presents the consortium block chain framework for revitalizing the wine market and enhancing transparency as the "right solution" of the nation's wine investment market. Blockchain governance can compensate for the shortcomings of the wine market because it guarantees desirable decision-making rights and accountability. Because the data stored in the block chain can be checked by consumers, it reduces the likelihood of counterfeit wine appearing and complements the process of unreasonably priced. In addition, digitization of assets resolves low cash liquidity and saves money and time throughout the supply chain through smart contracts, lowering entry barriers to wine investment. In particular, if the governance of the block chain is composed of 'chateau-distributor-investor' through consortium blockchains, it can create a desirable wine market. The production process is stored in the block chain to secure production costs, set a reasonable launch price, and efficiently operate the distribution system by storing the distribution process in the block chain, and forecast the amount of orders for futures trading. Finally, investors make rational decisions by viewing all of these data. The study presented a new perspective on alternative investment in that ownership can be treated like a share. We also look forward to the simplification of food import procedures and the formation of trust within the wine industry by presenting a framework for wine-owned sales. In future studies, we would like to expand the framework to study the areas to be applied.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.4
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• pp.425-437
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• 2007

Flow distribution of water and sedimentation rate were investigated to understand the hydrodynamics and settling characteristics of particulate materials in a constructed wetland for treatment of non-point sources pollutants, the Sihwa constructed wetland, Korea. The Sihwa constructed wetland is divided into three sub-wetlands(the Banwol, the Donghwa and the Samhwa wetlands) to treat the polluted water from three streams, the Banwol stream, the Donghwa stream and the Samhwa stream. From the results of water flow experiment using dye(Rhodamine 50WT Red), it was found that the water flow in the wetland was prevailing at the waterway and open water. Dye was spread slowly in the closed water area planted by plants. The mean hydraulic retention time(HRT) at the upper area of high wetland and lower wetland of Banwol, was found to be 34.1 hr at the upper area and 74.6 hr at the lower area respectively, totaling approximately 108.7 hr(4.5 days). The sedimentation rate was higher at lower area(sites of B, C and D) of the wetland than upper area(site of A which is settling zone). Based on the forecast for 20 years as to the amount of sediment that can be deposited in the open water in the future, the sediment depth of each area would be like this: A: 6.3 cm, B: 8.3 cm, C: 7.0 cm, D: 9.5 cm. The contents of organic materials in the sediment deposited within the sediment trap were found to be higher overly in the first investigation period which had much rainfall, and B, C and D areas were found to have an increased COD accumulation than A area. Also, nitrogen and phosphorus were found to increase in the down-stream of the wetland. The results of this study suggest that a sustainable research and management for the characteristics of water flow pattern and sedimentation changeable as time passes is needs to maintain or improve the efficiency of water treatment in the constructed wetland.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1092-1100
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• 2015

This thesis is a part of a case study conducted in order to understand the trends of the 16th~17th century Cheongju region bigwigs, and has examined the life and academic stream of Seogye Deuk-Yoon LEE (1553-1630) focused on the previous study, "The Great Family Genealogy of Ikjaegong of the Gyeongju LEE Family". Seogye Deuk-Yoon LEE learned from his father Seomgye Jam LEE, and GiSEO, Ji-Hwa PARK from an early age, and based the basic orientation of his studies on one's moral and religious self one's moral and religious self'. This is how he became to emphasize "Sohak" (an introductory book of Confucianism for children), and he made an effort to realize the world of Neo-Confucianism by distributing the 'YEO's Hyangyak(Lue-shih-hsiang-yueeh : autonomic regulations of the district areas)' published on "Sohak". Furthermore, he made great effort in education of the Cheongju by regarding it as his own mission to teach young scholars, continuing on the footsteps of his father. Considering this, Seogye was not only a Confucian scholar that devoted himself to 'Sugi(moral training of himself', but was also a practical scholar that committed his sense of social responsibility in ' teaching' and 'governing the people, who greatly affected the academic world of the regional bigwigs of the Cheongju during the 17th century. Furthermore, Deuk-Yoon LEE was a member of the 'Nangseongpalhyeon(eight wise men of the Cheongju region) together with his disciple Deok-soo LEE, who performed a core role in establishing the 'Gihohakpa(Capital and Chungcheong province School)' and 'Hoseosarim(forest of scholars in Chungcheong province)' of the Cheongju region. As a main figure in establishing the Sinhang Confucian academy, he prepared the socio-economic basis for the 'Gihohakpa' to take place in the Cheongju, and by academically associating with Sagye Jang-Seng Kim without regarding their conflicting parties, he became the bridge in allowing his disciple, Deok-Soo LEE to associate with the academic stream and the 'Gihohakpa'. Through such roles, he allowed the relatively easy establishment of the 'Gihohakpa' and 'Hoseosarim', which continued to Jang-Seng KIM and Si-Yeol SONG, in order to prepare the basis and establish the strength of its basis in the Cheongju region from the late 17th century.

• 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.