• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.4
• /
• pp.563-568
• /
• 2007

This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.

• International Commerce and Information Review
• /
• v.13 no.4
• /
• pp.229-249
• /
• 2011

The exchange rate change has been increased since the time when the floating exchange rate system was introduced in Korea. As a result, the increase of the exchange rate changes raised the risk in international trades in Korea. Also after Bretton Woods System broke down, the increasing exchange rate fluctuation raised the risk in international trade. The purpose of this dissertation is to study whether this incomplete pass-through exists in Korean export industry and furthermore to measure the markup rate of the export price using real data since Global Financial Crisis. The estimation results of the export price determination model by Error Correction Model shows that the export price of Korea has been greatly influenced by the export prices and exchange rates against U.S. Dollar of rival countries, domestic producer price as well as the Korean Won-U.S. Dollar exchange rate and also business coincidence index of U.S. in demand. Particularly, the pass-through rate of Korean Won-U.S. Dollar exchange rate to export price is estimated to be incomplete, which contrasts with the propositions of traditional exchange rate determination approach, e. g. elasticity approach, monetary approach, etc.

• The Journal of Society for e-Business Studies
• /
• v.19 no.4
• /
• pp.231-249
• /
• 2014

We define $21^{st}$ century as an amalgamation of globalization and localization, or Glocalization. Additionally, due to the increasing supply of smart phones and wide usage of social networking services, the ability to utilize such global and regional information has increased a coperation's competitiveness in its market, and even the business models have evolved from the conventional "production and distribution" to E-commerce, through which either a direct or a non-direct transaction is possible. My hypothesis is that the ability to adapt to this trend is possible through transfer of learning, and consequently, this will have an impact on company's performance. Thus, this thesis analyzes the mid- to the long-term impact of such ability and environmental factors on the performance and technology innovation of Korean companies in China. Ultimately, this study intends to engender a basic foundation for a corporation's management strategy in China. Finally this research focuses on those Korean companies in China only and on the proof of influential factors' impact on technological innovation and technological innovation's impact on those corporations' future performances. Section I is an abstract and section II, the case examines the uniqueness and current status of Korean companies in China identifies the concept and the definition of influential factors such as education-orientation, technological innovation, and performance, and then scrutinizes each factors through a closer look at their past researches. Section III explains the thesis model, the survey's method and target, the thesis, variable factors, the content, and the method of analysis. In section IV, the thesis is proved based on the outcome of the survey. The result in Section V highlights the high comprehension of technological innovation: both education-orientation and technological innovation prove to have a positive (+) correlation with the performance. The vision on education orientation proves to have a positive (+) influence on technological innovation. The vision on education-orientation and technological innovation prove to have a positive (+) influence individually on company's performance.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.8 no.2
• /
• pp.89-102
• /
• 2018

With the development of mobile technology, asset management on the Internet have also developed a lot. Drawing on Technology Acceptance Model, this study examines YUEBAO deployment to model consumers' purchase intention to use financial products offered online. In this study, we hypothesized that the characteristics of online asset management product will affect the purchase intention through perceived usefulness and conduct empirical analysis on Chinese consumers. In the study model, the independent variables were considered to include individual involvement, experience, product protection, corporate credibility, convenience, mobility, and familiarity. In addition, the parameters constitute the usefulness, and the dependent variable is the purchase. The results are as follows. First, YUEBAO's complementarity, corporate credibility, convenience, and familiarity have a significant influence on YUEBAO's usefulness. Second, The YUEBAO's usefulness has a noticeable effect on the purchase intention. To perceive the high usefulness, the practicality strategy of enhancing the protection property, corporate reliability, convenience and familiarity of the online asset management product is needed. The study of consumer purchase behavior and consumer purchase intention of online wealth management products is very valuable for academic and practical work.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.145-165
• /
• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• Journal of Digital Convergence
• /
• v.15 no.8
• /
• pp.477-487
• /
• 2017

This paper releases strategies for firms that wish to enter into online circulation business in China. SWOT based on a Korean manufacturing company teaches us better way to approach Chinese online market. Chinese online market is under oligopoly, where Alibaba and Jingdong account for about 80% of the total market. Game theory is used as a measure of threat and opportunity between Korean manufacturer and Chinese online market retailer. Game shows that they are easy to accept opportunity and sales uplift rather than low risk. Analysis shows that Korean companies should improve its products and brand competitiveness in the offline market before entering into the Chinese online retail market. They need to prepare a localization model.

• Journal of Intelligence and Information Systems
• /
• v.22 no.4
• /
• pp.1-18
• /
• 2016

Collaborative Filtering (CF) predicts the focal user's preference for particular item based on user's preference rating data and recommends items for the similar users by using them. It is a popular technique for the personalization in e-commerce to reduce information overload. However, it has some limitations including sparsity and scalability problems. In this paper, we use a method to integrate social network information into collaborative filtering in order to mitigate the sparsity and scalability problems which are major limitations of typical collaborative filtering and reflect the user's qualitative and emotional information in recommendation process. In this paper, we use a novel recommendation algorithm which is integrated with collaborative filtering by using Social SVD++ algorithm which considers social network information in SVD++, an extension algorithm that can reflect implicit information in singular value decomposition (SVD). In particular, this study will evaluate the performance of the model by reflecting the real-world user's social network information in the recommendation process.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.3
• /
• pp.589-595
• /
• 2009

This paper proposes an object-oriented software development guidance and an evaluation index for the productivity related to Spring Framework 2.5. Spring Framework is a known successful open source standard model for lightweight container architecture. Non EJB and the EBJ architecture to resolve the problem with benefits to support the new architecture is a lightweight container architecture. This architecture, such as the EJB, but not heavy, to provide all of the architecture is possible. The lightweight container architecture is most often used in business spring framework is well-known architecture. Therefore, this research has the Non EJB and the EJB to solve the advantages and disadvantages developed to support the latest spring framework 2.5 lightweight container architecture based on the design and implementation of a hotel reservation system with the objective through the specification of the software previously to provide guidance to development productivity.

• International Area Studies Review
• /
• v.22 no.3
• /
• pp.201-219
• /
• 2018

This paper suggested a theoretical model, in which a security-based(secured loan, non-secured loan) credit agreement determines the form of corporate cost function through a loaning company's cost minimization in the light of a company which behaves monopolistically in product markets. Also, this paper analyzed the influence of a corporate credit agreement on market equilibrium, and economic welfare in product markets. As a result, it was found that in case a company, whose equity capital is small, implements borrowing based on a secured loan from a financial institution, the company comes to face borrowing restraints, in which the company has no choice but to get a loan within the scope of securities. When a company offers its capital goods, i.e. a production factor, as a security, there occurs a distortion to the production factor input ratio. Meanwhile, when a company comes to get a loan based on an unsecured loan, for which the interest rate is high, marginal cost rises; accordingly, the company comes to choose a credit agreement aiming at maximizing its profits. However, a company's choice of a credit agreement is not quite desirable from a consumer's viewpoint, and from the whole economic point of view; overall, such a choice is likely to aggravate economic welfare.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.221-241
• /
• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.