• 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 Distribution Science
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• v.7 no.4
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• pp.47-56
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• 2009

The purpose of this study is to present an alternative improving the efficient and reasonable of the physical distribution system management is influenced by many factors. Therefore, the study depends on the documentary method and survey method to achieve the purpose of this study. The major components of a physical distribution system are refers to as elements, include warehouse·storage system, transportation system, inventory system, physical distribution information system. The factors used in this study are ① factor of product(quality·A/S·added value of product·adaption of product·technical competitive power to other enterprises), ② factor of market(market channel·kinds of customer·physical distribution share), ③ factor of warehouse·storage(warehouse design·size·direction·storage ability·warehouse quality), ④ factor of transportation(promptness·reliability·responsibility·kinds of transportation·cooperation united transportation system·national transportation network), ⑤ factor of packaging (packaging design·material·educating program·pollution degree measure program), ⑥ factor of inventory(ordinary inventory criterion·consistence for inventories record), ⑦ factor of unloaded(unloaded machine·having machine ratio), ⑧ factor of information system (physical distribution quantity analysis·usable computer part), ⑨ factor of physical distribution cost(sales ratio to product) ⑩ factor of physical distribution system(physical distribution center etc). The implication of this study can be summarized as follows: ① In firms that have not adopted a systems integrative approach, physical distribution is a fragmented and often uncoordinated set of activities spread throughout various functions with function having its own set of priorities and measurements. ② The physical distribution is recognized as more an important strategic factor than a simple cost reduction factor, ③ It can be used a strategic competition tool to enterprise.

• Trans-
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• v.9
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• pp.17-36
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• 2020

This study is aimed at discussing the possibility of content production, utilization and expansion, focusing on the exhibition case of Danwon Art Museum run by Ansan Cultural Foundation. In 1991, the Ministry of Culture, Sports and Tourism named Ansan as the City of Danwon since it is believed to be the hometown of Danwon Kim Hong-do (1745~?), a painter of the late Joseon Dynasty and a well-known master of genre painting. As a result, Ansan is making various efforts to utilize Danwon Kim Hong-do for its unique resource through internal and external business such as the creation of Danwon Sculpture Park, the operation of Danwon Art Museum, and the planning of Danwon Kim Hong-do Festival. However, the biggest problem with Ansan is that there are not many collections of Kim Hong-do. Ansan has owned a total of six works as of May this year: a deer and a boy, flowers and a bird, A view of clouds on the water, Daegwallyeong, Yeodongbin, A way to Singwangsa. Accordingly, Danwon Contents Center has set up a vision to systematically collect, preserve, and display various visual and artistic materials related to Kim Hong-do, offering high-quality information based on digital data. In other words, it is a complex cultural information agency of One-Source Multi-Use, which combines the functions of libraries, archives and art galleries so that visitors' desire is satisfied. It reflects the contemporary trend of overcoming the limitations of the ancient paintings and satisfying the role and function of the art museum. From the opening of the Danwon Contents Hall, the original work of Kim Hong-do has been interpreted and produced as media contents or recreated as a new form of art by modern artists. Exhibition using technologies such as touch screen and 'deep zoom' helps visitors to heighten their experience of the archives and get inside the world of the genius painter.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Management & Information Systems Review
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• v.38 no.2
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• pp.139-163
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• 2019

The purpose of this study is to identify the influence of the high performance and the work system recognized by the organizational members participating in the NCS enterprise utilization consulting on the organizational effectiveness and the influence of the organizational performance. In order to achieve the purpose of this study, the research hypotheses were verified by applying multiple regression analysis and co-integration regression analysis on the 145 usable results from using the statistical program of SPSS 24.0. The results of this study are as follows. First, hypothesis 1, the analysis of positive(+) influence on organizational effectiveness on the characteristics of the system (selection system, evaluation and compensation, education and training opportunity, member participation system, job stability) was adopted due to its' positive(+) and significant relationship. Second, hypothesis 2, an analysis that the organizational effectiveness would have a positive impact on organizational performance was adopted due to its' proven work. However, its' effectiveness will need verification. Third, regression analysis was conducted to confirm the role of the hypothesis 3, personnel management effectiveness, between the high performance and the work system (selection system, evaluation/compensation, education/training opportunity, member participation system, job stability). As a result, Hypothesis 3 showed that the selection system (H3-1), evaluation and compensation(H3-2), education and career opportunities(H3-3), member participation system(H3-4), job stability(H3-4) showed that organizational effectiveness has a moderating role in HR effectiveness. The main implication of this study is that the positive and working system recognized by the organizational members has positive affects on organizational effectiveness and organizational performance. In order to enhance the substantiality management system through improvement of organizational performance, a regular implementation plan on a high-performance working system along with education & training system to improve organizational effectiveness is required so that the members of the organization could form a consensus. Second, as a result of confirming the moderating effects of HR management effectiveness, the moderating role of HR management system and organization effectiveness was verified positive. Therefore, this study shows that the part that verifies both organizational effectiveness and organizational performance through high-performance working system and the part that confirmed the role of control between high-performance work system and organizational effectiveness are the biggest difference from the previous research.

• The Korean Journal of Archival Studies
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• no.28
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• pp.35-81
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• 2011

Manhwa is a mass media (to expose all faces of an era such as politics, society, cultures, etc with the methodology of irony, parody, etc). Since the Manhwa records is primary culture infrastructure, it can create the high value-added industry by connecting with fancy, character, game, movie, drama, theme park, advertising business. However, due to lack of active and systematic aquisition system, as precious Manhwa manuscript is being lost every year and the contents hard to preserve such as Manhwa content in the form of electronic records are increasing, the countermeasure of Manhwa contents management is needed desperately. In this study, based on these perceptions, the need of Manhwa records management is examined, and the characteristics and the components of Manhwa records were analyzed. And at the same time, the functions of record management process reflecting the characteristics of Manhwa records were extracted by analyzing various cases of overseas Cartoon Archives. And then, the framework of record-keeping regime was segmented into each of acquisition management service areas and the general Manhwa records archiving strategy, which manages the Manhwa contents records, was established and suggested. The acquired Manhwa content records will secure the context among records and warrant the preservation of records and provide diverse access points by reflecting multi classification and multi-level descriptive element. The Manhwa records completed the intellectual arrangement will be preserved after the conservation in an environment equipped with preservation facilities or preserved using digital format in case of electronic records or when there is potential risk of damaging the records. Since the purpose of the Manhwa records is to use them, the information may be provided to diverse classes of users through the exhibition, the distribution, and the development of archival information content. Since the term of "Manhwa records" is unfamiliar yet and almost no study has been conducted in the perspective of records management, it will be the limit of this study only presenting acquisition strategy, management and service strategy of Manhwa contents and suggesting simple examples. However, if Manhwa records management strategy are possibly introduced practically to Manhwa manuscript repositories through archival approach, it will allow systematic acquisition, preservation, arrangement of Manhwa records and will contribute greatly to form a foundation for future Korean culture contents management.

• Management & Information Systems Review
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• v.38 no.1
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• pp.201-223
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• 2019

The purpose of this study is to verify empirically the causal relationship between service quality, ease of use, customer satisfaction, and repurchase intention of mobile shopping mall. And this study is to investigate the ease of use and customer satisfaction mediating effect of between service quality and repurchase intention. Therefore, 323 university students in Jeonnam area were surveyed and the structural equation model was derived based on previous research. Service quality of mobile shopping mall make a significant effect on using easiness, purchasing satisfaction and repurchase intention. However, among service quality of mobile shopping mall, service scape like mobile interface and site design made a positive effect on purchasing satisfaction, but did not any effect on repurchase intention. In other words, service quality factors that make positive effects on customer's pleasant using and repurchase intention make a positive effect on repurchase intention when providing and using the service customer wants faithfully rather than external part of the site and mutually influencing attitude or behavior well. The implications suggested by this study are as follows. First, service quality of mobile shopping mall makes a significant effect on repurchase intention, so it's necessary to improve CS service system so as to treat customers' inquiries or inconveniences actively during mobile shopping and return and refund of defective products quickly and conveniently. And, in addition to the finally used factors in analysis process, benefits using customers' grade by number of purchases, such as various events, coupons, reserve, etc. and active contents marketing strategies providing more various pleasures and values of shopping are necessary. Second, satisfaction of mobile shopping mall makes a positive effect on repurchase intention, so visiting of site and repurchasing of product are continuously done as customers' satisfaction on shopping mall is increasing. Therefore, shopping mall site requires differentiation of contents, exact plan and practice of service, marketing, etc. so that customers can feel more satisfaction. This study is significant as it systematically analyzed concepts of components that service quality of mobile shopping mall makes an effect on using easiness, purchasing satisfaction, and repurchase intention, verified the relations, systematized it by theoretical structure, and widened the understanding of effects making an effect on repurchase intention.

• Korean Journal of Heritage: History & Science
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• v.53 no.4
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• pp.136-149
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• 2020

It is generally believed that Douglas Fir timber imported from North America is used in repair work for Korean wooden heritage sites due to an insufficient supply of extra-large sized Korean pine timber. Based on this understanding in the cultural heritage repair field, Cultural Heritage Repair Business Entities ("CHRBE") prefer North American Douglas Fir timber which is more easily acquired on the market than large Korean pine timber. However, if CHRBE use large quantities of foreign-origin wood in the heritage repair field, this presents the threat of negative domestic impacts on cultural heritage such as breaching the preservation principal and ultimately weakening material authenticity. Therefore, this study aims to investigate the current supply status of large Korean pine timber through examination of existing research, interviews with experts engaged in CHRBE, and timber mills. With this information, the authors seek to identify whether the market supply of large Korean pine timber is indeed insufficient or not. In addition to this, this paper identifies the reasons why large Korean pine timber is not widely used if such timber supply is actually sufficient. In order to propose suggestions regarding the issues above, the authors study the distribution channel for large Korean pine timber and the price spectrum of this timber through examination of price information from the public agencies under the Korea Forest Service, research papers from the Cultural Heritage Administration, and estimation documents from timber mills. This paper also identifies two main opinions about why Korean timber has not been commonly used in the Korean heritage repair field. The first opinion is that the supply of large Korean pine timber really is insufficient in Korea. However, the second opinion is that it is hardly used due to inappropriateness of the government's procurement and estimation system, despite the fact that the supply of the timbers on the market is actually sufficient. Through the aforementioned research, this paper comes to the conclusion that the second opinion has strong grounds in many aspects. In terms of suggestions, alternative routes are proposed to stimulate the use of large Korean pine timber via supply by the 'Korea Foundation for Traditional Architecture and Technology' and surveys of the price spectrum of the timber, etc.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.