• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.183-200
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• 2009

Collaborative Filtering is one of the most used recommender systems. However, basically it cannot be used to recommend new products to customers because it finds products only based on the purchasing history of each customer. In order to cope with this shortcoming, many researchers have proposed the hybrid recommender system, which is a combination of collaborative filtering and content-based filtering. Content-based filtering recommends the products whose attributes are similar to those of the products that the target customers prefer. However, the hybrid method is used only for the limited categories of products such as music and movie, which are the products whose attributes are easily extracted. Therefore it is essential to find a more effective approach to recommend to customers new products in any category. In this study, we propose a new recommendation method which applies centrality concept widely used to analyze the relational and structural characteristics in social network analysis. The new products are recommended to the customers who are highly likely to buy the products, based on the analysis of the relationships among products by using centrality. The recommendation process consists of following four steps; purchase similarity analysis, product network construction, centrality analysis, and new product recommendation. In order to evaluate the performance of this proposed method, sales data from H department store, one of the well.known department stores in Korea, is used.

• Journal of Distribution Research
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• v.10 no.4
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• pp.1-22
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• 2005

The major objectives of this study are twofold. The first is to discover which in-store tactics influence store performance when a retailer implements category management in variety enhancer and fill-ins categories. The second is to analyze how and why specific in-store tactics achieve better or worse performance than other in-store tactics across categories. The data were collected using scanner data and direct observations in 'A' discount store which is one of the representative discount stores in Korea. The in-store tactics were measured by product assortment, temporary price discount, price and non-price promotion, and shelving. The store performance was measured by sales and gross margin return on inventory investmant(GMROI). Empirical results analyzed by multiple regression were as follows: In variety enhancer category, the significant factors affecting sales were product assortment, temporary price discount, price promotion, and shelving. Non-price promotion also influenced GMROI positively but product assortment impacted on GMROI negatively. In fill-ins category, the significant factors affecting sales and GMROI were product assortment and shelving. However, the other factors such as temporary price discount, price promotion, and non-price promotion had no significant influence on both sales and GMROI. This paper presents a number of theoretical and managerial implications of the empirical results and concludes by addressing limitations and future research directions.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2006.11b
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• pp.397-409
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• 2006

연구결과는 학술적인 논문, 기술의 기반이 되는 특허, 기술이 체화된 상품 등 다양한 방법으로 도출되고 있다. 현재까지 연구 성과를 측정하는데 있어서 수치로 표시되는 정량적인 평가는 용이하게 활용하고 있다. 하지만 질적인 우수성을 평가하기 위한 정성적인 방법은 많은 연구를 필요로 한다. 각기 다른 목적이나 용도로 만들어진 평가지표나 방법론이 적절하게 적용하여 평가지표로 적용되어 지기는 힘든 상황이다. 특히 연구자나 기관 등의 평가 시 연구논문을 활용한 계량정보기법을 근간으로 하는 다양한 방법이 많은 연구자들을 통해 제시되고 있다. 또한 학술적인 논문뿐아니라 산업으로 대표되는 특허의 평가시도 유사한 방법을 활용하고 있다. 문헌의 경우는 SCI DB를 활용하여 정성적인 부분을 대체하고 있다. 하지만 산업으로 대표되는 특허의 정성 평가는 많이 활용하지 못하고 있다. 이에 특히 문헌 및 특허의 인용정보를 대상으로 최근 제안되고 있는 연구 성과 지표인 h-지수 등을 활용하여 사례를 분석하여 보고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.101-108
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• 2020

Recommender system is a must-have feature of e-commerce, since it provides customers with convenience in selecting products. Collaborative filtering is a widely-used and representative technique, where it gives recommendation lists of products preferred by other users or preferred by the current user in the past. Recently, researches on the recommendation system using deep learning artificial intelligence technologies are actively being conducted to achieve performance improvement. This study develops a collaborative filtering based recommender system using restricted Boltzmann machines of the deep learning technology by utilizing user ratings. Moreover, a learning parameter update algorithm is proposed for learning efficiency and performance. Performance evaluation of the proposed system is made through experimental analysis and comparison with conventional collaborative filtering methods. It is found that the proposed algorithm yields superior performance than the basic restricted Boltzmann machines.

• Science of Emotion and Sensibility
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• v.11 no.2
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• pp.193-206
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• 2008

This paper describes to experiments on the estimation of tourist images in Busan. Preliminary tasks were performed to find out the condition of tourism souvenir and products proper to ocean city with situation in Busan. This survey analyzed by factor analysis taken in local residents, foreigners and other city. Result of factor analysis shows that the local residents and others think of Busan as similar image both. Outsider recognized Busan to tourist attractions of seaside environment and tourist facilities, and have good image of sightseeing and shopping in city. The local people remind Busan as progressive, feels sweet, activity, friendly image beside that foreign tourist remind as clean, modern, abundant, and others think as stable, changing, novelty, friendly. To abstract 8 factors among the images, take out of relevance to condition of tourist and image factors. Important factors for development of tourist industry in Busan are a feeling safe, clean, novelty in priority. And find way that enhancing image to abundant, friendly, activity were proper to tourist industry.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.489-496
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• 2011

In designing cultural product, end user experiences, feelings and satisfaction with form are one of the most important factors. However, the design processes of cultural product typically depend on traditional methods or intuitive, designer driven methods for generating and evaluating form concepts. We applied sensibility evaluation, analysis method and contemporary digital vehicle design process to cultural product. This paper describes the implementation process of internet based design support system that helps assess user's sensibilities of forms created in each stages of design process. The form focused sensibility evaluation approach provides designers with fast and various feedbacks about their design in each stage. Practical application for exterior design of tourist cart of slowcity(ciitaslow) Shinan was illustrated.

• Information Systems Review
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• v.10 no.2
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• pp.195-209
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• 2008

Customers' needs change every moment. Profitability of stores can't be increased anymore with an existing standardized chain store management. Accordingly, a personalized store management tool needs through prediction of customers' preference. In this study, we propose a recommending procedure using dynamic customers' preference by analyzing the transaction database. We utilize self-organizing map algorithm and association rule mining which are applied to cluster the chain stores and explore purchase sequence of customers. We demonstrate that the proposed methodology makes an effect on recommendation of products in the market which is characterized by a fast fashion and a short product life cycle.

• International Commerce and Information Review
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• v.16 no.3
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• pp.73-96
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• 2014

The purpose of this research is to evaluate a short-term export demand forecasting model reflecting individual passenger vehicle brands and market characteristics by using Vector Autoregressive (VAR) models that are based on multivariate time-series model. The short-term export demand forecasting model was created by discerning theoretical potential factors that affect the short-term export demand of individual passenger vehicle brands. Quarterly short-term export demand forecasting model for two Korean small vehicle brands (Accent and Avante) were created by using VAR model. Predictive value at t+1 quarter calculated with the forecasting models for each passenger vehicle brand and the actual amount of sales were compared and evaluated by altering subject period by one quarter. As a result, RMSE % of Accent and Avante was 4.3% and 20.0% respectively. They amount to 3.9 days for Accent and 18.4 days for Avante when calculated per daily sales amount. This shows that the short-term export demand forecasting model of this research is highly usable in terms of prediction and consistency.

• The Research Journal of the Costume Culture
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• v.24 no.6
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• pp.803-816
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• 2016

The purposes of this study were to investigate the influences of channel assessments on the usage of multi-channels by product types, and the differences in the usage of multi-channels among product types in buying decision making process for fashion products. Data were collected from 510 consumers in their 20s to 50s with purchasing experiences through multi-channel distribution system and living in Seoul and Kyunggi province; 491 were analyzed after deleting incomplete questionnaires. Factor analysis, multiple regression analysis and one-way ANOVA were used for statistical analysis by using SPSS 18.0. The results were as follows: 5 factors were extracted for channel assessment: utility, accuracy, risk, price benefit and sharing information. Price benefits, utility and sharing information for online channel tended to influence positively on the usage of online channel and online+offline channels. Accuracy and low perceived risk of offline influenced positively on offline and on+offline channel usages. The usage levels of on-line and off-line channels for cosmetics were significantly lower than the usage levels for clothes and accessories on information search, evaluation of alternatives, and purchase stages. Significant differences were also found in the usage levels of multi-channels (on+off-line) on information search and evaluation of alternatives stages. The usage levels of the multi-channels for clothes were the highest followed by those of accessories and cosmetics in order.

• The Journal of Information Technology
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• v.5 no.3
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• pp.97-119
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• 2002

It is Study Service Model for Case Study of Internet Bank. With most major full service banks having launched transactional Internet banking, attention is shifting to the realities of managing the Internet channel as a profitable component of an overall delivery strategy. In addition to examining Case Study and Internet Bank Model. Services of Internet Banking available through the Internet are as follows. 1. credit card loans, personal loans. 2. high-yield financial products. 3. insurance products. 4. securities products. 5. Case study of Foreign Internet Banking(ING, BNP, HSNC, City Bank). The study reviewed fields, including financial services, customer service, Website formation and design, convenience of use and system safety, Internet Banking Model, and many related areas. Internet Banking earned high marks in most fields. This Study review focuses on the following: Understanding and meeting consumer expectations for us ability, site performance and functionality. Integrating the Internet channel into overall marketing, product delivery and customer service strategies. Strategies to increase customer satisfaction with Internet Banking and to attract new Internet bankers. therefore this study review activity model concretion of Internet Banking Model and Case Study.