• Journal of the Korean Society for Precision Engineering
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• v.18 no.5
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• pp.23-28
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• 2001

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.353-367
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• 2003

본 연구에서는 중소기업들이 중견기업으로 성장할 때까지 필요로 하는 경영, 자금, 기술, 마케팅, 물류 등 Total Solution 차원의 중소기업지원시스템 중 마케팅ㆍ유통과 관련 협력상거래(collaborative commerce)라는 개념을 도입하여 오프라인과 온라인이 결합되어 대전ㆍ충남 중소기업간의 협력, 제휴를 지원하고, 나아가 대기업, 학계, 벤처캐피탈들이 참여하여 교류할 수 있는 정보공유와 만남의 장을 제공함으로써, 협력상거래 포탈 사이트를 구축하기 위한 이론적 모형을 제시ㆍ구축하고자 한다. 협력상거래 포탈의 기술적 정의는 중소기업간에 인터넷을 통하여 마케팅ㆍ유통과 관련한 기업핵심정보와 비즈니스 프로세스를 공유함으로써 효율적인 협업 전자상거래를 가능하게 하는 모든 기술적 요소의 집합이라 할 수 있다. 협력상거래 포탈의 협업적 프레임워크 기능 요구사항은 \circled1Integration of product ＆ process information, \circled2Extensibility and flexibility of framework, \circled3Platform independence, \circled4Interdependence and modularity of services, \circled5Interoperability among services, \circled6Accessibility of legacy system(ERP, SCM, CRM) 등이다.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.67-74
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• 2011

Collaborative recommendation is one of the most widely used methods of automated product recommendation in e-Commerce. For analyzing the customer's preference, traditional explicit ratings are less desirable than implicit ratings because it may impose an additional burden to the customers of e-commerce companies which deals with a number of products. Cardinal scales generally used for representing the preference intensity also ineffective owing to its increasing estimation errors. In this paper, we propose a new way of constructing the ordinal scale-based customer profile for collaborative recommendation. A Web usage mining technique and lexicographic consensus are employed. An experiment shows that the proposed method performs better than existing CF methodologies.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.191-194
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• 2003

As internet commerce grows, many company has begun to use a CF (Collaborative Filtering) as a Recommender System. To achieve an accuracy of CF, we need to obtain sufficient account of voting scores from customers. Moreover, those scores may not be consistent. To overcome this problem, we propose a new recommendation scheme using binary user-item matrix, which represents whether a user purchases a product instead of using the voting scores. Through the experiment regarding this new scheme, a better accuracy is demonstrated.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.597-614
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• 2001

Level 0: The Marekting Buzzword : □Confusion between DMU and Visualization □Having a Mobile Data Viewer/Analyser is Anyway a Prerequisite Level 1: Digital Pre-Assembly (DPA): □Building Digital Prototype before Physical Build □Usually a job for Packaging or Prototype Teams □Usually no time Left to take Feed-back into account before Actual Build Level 2: Design in Context: □All Designers within Car Maker do Local DMU before DPA Level 3: Design in Extended Context □Design in Context Expanded to Suppliers(omitted)

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.229-229
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• 2004

CIMdata에 따르면, PDM(Product Data Management) 시스템은 엔지니어와 관련 사람들이 제품정보와 제품 개발 프로세스를 관리하는 것을 도와주는 도구이다. 이러한 PDM 시스템은 정보기술의 발전과 인터넷 환경의 급속한 발전에 따라 CPC (Collaborative Product Commerce) 페러다임에 포함되고 있다. Aberdeen Group에서는 CPC를 '제품의 라이프사이클인 제품설계, 엔지니어링, 생산과 구매를 포함한 조달, 판매, 마케팅, 현장 서비스와 전세계 고객들을 Web으로 묶는 SW 및 서비스' 라고 정의한다.(중략)

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.510-514
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• 2004

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is known to be the most successful recommendation technology, but its widespread use has exposed some problems such as sparsity and scalability in the e-business environment. In this paper, we propose a recommendation methodology based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of original CF-based recommender systems. Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, so leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. Several experiments on real e-commerce data show that the proposed methodology provides higher quality recommendations and better performance than original collaborative filtering methodology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.970-977
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• 2020

The collaborative filtering recommendation technique has been the most widely used since the beginning of e-commerce companies introducing the recommendation system. As the online purchase of products or contents became an ordinary thing, however, recommendation simply applying purchasers' ratings led to the problem of low accuracy in recommendation. To improve the accuracy of recommendation, in this paper suggests the method of collaborative filtering that analyses product reviews and uses them as a weighted value. The proposed method refines product reviews with text mining to extract features and conducts sentiment analysis to draw a sentiment score. In order to recommend better items to user, sentiment weight is used to calculate the predicted values. The experiment results show that higher accuracy can be gained in the proposed method than the traditional collaborative filtering.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.93-97
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• 2002

In this paper, we propose personalized recommendation techniques based on multidimensional scaling (MDS) method for Business to Consumer Electronic Commerce. The multidimensional scaling method is traditionally used in marketing domain for analyzing customers' perceptional differences about brands and products. In this study, using purchase history data, customers in learning dataset are assigned to specific product categories, and after then using MDS a positioning map is generated to map product categories and alternative advertisements. The positioning map will be used to select personalized advertisement in real time situation. In this paper, we suggest the detail design of personalized recommendation method using MDS and compare with other approaches (random approach, collaborative filtering, and TOP3 approach)

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.139-157
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• 2002

The rapid growth of e-commerce has made both companies and customers face a new situation. Whereas companies have become to be harder to survive due to more and more competitions, the opportunity for customers to choose among more and more products has increased. So, the recommender systems that recommend suitable products to the customer have an important position in E-commerce. This research introduces collaborative filtering based recommender system which helps customers find the products they would like to purchase by producing a list of top-N recommended products. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is used to select target customers, who have high possibility of purchasing recommended products. We applied the recommender system to a Korean department store. The methodology is evaluated with the analysis of a real department store case and is compared with other methodologies.