• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.202-204
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• 2005

상품 온톨로지는 온톨로지 (Ontology) 구성요소인 개념 (Concept) 과 속성 (Property)이 상품 도메인에 특화된 온틀로지이다. 본 논문에서는 대용량을 특징으로 하는 상품 온톨로지를 표현함에 적용되어 질 수 있는 Hyperbolic Tree, Cluster Map, 그래프 비주얼라이제이션을 살펴보고, 계층을 갖는 개념을 표현하는 데 좋은 Hyperbolic과, 속성을 잘 표현 할 수 있는 Cluster Map을 상품 온톨로지에 유리한 비주얼라이제이션으로서 제안한다.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.381-391
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• 2020

The purpose of this study is to investigate the relationship between meal kit product selection attributes, purchasing behavior, and satisfaction. The sampling of the study was conducted for 1 month for customers who have experience in using meal kit products recently launched by a restaurant company, and 287 copies of the questionnaire were used for analysis. For the hypothesis verification, regression analysis was performed using SPSS 20.0 package. As a result of analysis, first, the meal kit product selection attributes and purchase behavior of Hypothesis 1 have a significant effect on diversity (β = .026) and quality (β = .927). Hypothesis 2, meal kit product selection attributes and satisfaction have a significant effect on convenience (β = .503) and price (β = .121). Third, in the purchasing behavior of Hypothesis 3, the purchasing behavior (β = .561) has a significant effect on satisfaction. Lastly, this study is expected to provide basic data for researchers performing meal kit product related research, and to provide a rationale for suggesting direction for product development in a food service company and using marketing strategies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.460-461
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• 2018

The traditional recommendation system is developed on the assumption that users behave independently, and have problem of readability and efficiency are inferior due to simply sort products or lack of function for associate product attributes with user's taste. To solve this problem in this study we propose a system that provides user customized information that the analysis of the unstructured review data with the purchase histories of users processed with meaningful information after crawling product review data using text mining with R. This allows to help user make decisions can be provided only necessary information without analyze massive amounts of products review data.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.398-406
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• 2009

This study intends to provide support for tourism industry to establish marketing strategies for selling the travel products on which it should focus on more by examining the difference on selection of travel product attributes by tourism action. This research utilized cluster analysis and ANOVA. The cluster analysis identified four cluster: logical tourism, weak conspicious tourism, value tourism, conspicuous and value tourism. ANOVA analysis showed that four cluster were different in terms of tourism action such as transportation accommodation facilities, tourist attractions, shopping and meals except for travel service, entertainment.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.637-640
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• 2000

인터넷 상에는 많은 전자 쇼핑몰들이 개설되어 있고 계속해서 빠른 속도로 증가하고 있다. 전자 쇼핑몰들이 증가함에 따라 동일한 품목과 동일한 소비자 계층을 대상으로 하는 경쟁적 쇼핑몰들이 증가하는 추세에 있다. 이러한 환경 속에서 다른 전자 쇼핑몰들에 대한 우위를 점하기 위해서는 효과적이고 차별적인 마케팅전략이 절실하게 요구된다. 그러한 전략의 한 가지로서 소비자에게 적합한 상품을 추천하는 쇼핑몰이 등장하게 되었다. 그러나 대부분의 쇼핑몰은 아직까지 소비자와 상품의 연관성을 찾아서 소비자에게 유용한 정보를 제공하는 방식을 쓰지 않고 있다. 따라서 이 논문에서는 고객과 제품에 관한 정보를 기반으로 소비자의 구매의사 결정 패턴을 고려하여 가장 고객에게 적합한 상품을 추천해 주는 3차원적 모델을 제시하고, 모델 구성에 필요한 3차원 속성(제품 특성, 소비자의 유형, 구매의사결정 유형)의 분류에 대하여 연구한다.

• Proceedings of the CALSEC Conference
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• 1999.11a
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• pp.108-116
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• 1999

최근 사이버 쇼핑몰에서 기업과 고객간의 제품 구매 활동을 도와주는 검색 에이전트 시스템에 대한 연구가 활성화되고 있다. 그러나 많은 비교 검색 에이전트 시스템들은 고객이 입력한 키워드 기반의 검색 목록만을 체공하고 있으며, 검색된 유사상품 목록 중에서 어떤 상품이 적합한지 선정해 주는 의사결정지원 기능은 충분히 제공하지 못하고 있다 본 논문에서는 인터넷 쇼핑몰 환경을 기반으로 키워드 입력에 의한 제품 검색뿐만 아니라 고객 선호도가 높은 제품 선정을 지원해 주는 의사결정지원 시스템을 제안한다. 제안한 의사결정지원 시스템은 고객이 입력한 각 속성들의 표준계수와 가중치를 점수화 하여 선호도가 높은 상품을 우선적으로 제시함으로써 고객의 상품 선정을 위한 의사결정을 지원하도록 하였으며, 또한 각 속성별 다양한 반영 비율에 따라 어느 제품이 전체적으로 적합한지를 분석하는 민감도 분석 기능을 추가하였다.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.159-163
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• 2006

우리나라 여행업은 1989년 이후 해외여행 전면 자유화 실시 이후 다양한 유통채널을 통해 양적 질적 발전을 이루어왔다. 그러나, 학문적 관점에서는 다양화된 유통채널에 대한 다양한 접근은 아직까지 부족한 실정이었다. 따라서, 본 연구에서는 선행연구 검토를 통한 다각적인 접근을 통해 서비스 기업의 상품 선택속성에 관한 이론적 토대를 제시하는 데 일조하는 데 연구목적이 있다.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.201-216
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• 2008

Nowadays various new items are available, but limitation of searching effort makes it difficult for customers to search new items which they want to purchase. Therefore new item providers and customers need recommendation systems which recommend right items for right customers. In this research, we focus on the new item recommendation issue, and suggest preference boundary- based procedures which extend traditional content-based algorithm. We introduce the concept of preference boundary in a feature space to recommend new items. To find the preference boundary of a target customer, we suggest heuristic algorithms to find the centroid and the radius of preference boundary. To evaluate the performance of suggested procedures, we have conducted several experiments using real mobile transaction data and analyzed their results. Some discussions about our experimental results are also given with a further research area.

• Journal of Korea Multimedia Society
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• v.11 no.2
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• pp.257-268
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• 2008

Multimedia recommendation systems analyze user preferences and recommend items(multimedia contents) to a user by predicting the user's preference for those items. Among various kinds of recommendation methods, collaborative filtering(CF) has been widely used and successfully applied to practical applications. However, collaborative filtering has two inherent problems: data sparseness and the cold-start problems. If there are few known preferences for a user, it is difficult to find many similar users, and therefore the performance of recommendation is degraded. This problem is more serious when a new user is first using the system. In this paper, we propose a method of generating additional feature of users and items into CF to overcome the difficulties caused by sparseness and improve the accuracy of recommendation. In our method, we first generate additional features by using the probability distribution of feature values, then recommend items by applying collaborative filtering on the modified data to include additional features. Several experimental results that show the effectiveness of the proposed method are also presented.

• The Journal of Society for e-Business Studies
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• v.11 no.3
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• pp.35-51
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• 2006

Information visualization plays an important role to provide a conceptual comprehension of the information and should be adjusted and featured to reflect the characteristics of the domain to which it is applied. Product searching in e-Commerce is not an exception. In this paper, we present visualization techniques that are specified for effectively browsing the results of product searching. We considered visualization techniques including MapNet and Cluster Map and modified them to reflect the characteristics of the product domain. We consider two types of search queries: category searching and product property searching, and make it possible to include more detailed product semantics in their search criteria. We also provide a query refinement mechanism so that even users with rack of understanding the products may rewrite their queries for better results.