• 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 CALSEC Conference
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• 2001.08a
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• pp.439-448
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• 2001

Data Alignment is achieved when all trading partners information systems are maintained automatically synchronising with the suppliers information systems on a continuing basis. Electronic catalogues facilitate the ongoing synchronisation of data between trading partners and large retailers in United States and Canada use electronic catalog in order to get rid of non-value added paperwork and manual reconciliation. Data Alignment will dramatically improve the effectiveness of E-Commerce and Supply Chain initiatives including electronic Marketplaces, Collaborative Planning and Forecasting and continuous replenishment processes.

• 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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• 2002.05a
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• pp.342-346
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• 2002

본 논문에서는 전자상거래 발전 단계 및 프로세스 공유에 대만 문헌 연구, 프로세스 표준화 작업. 현행프로세스 유형 및 현행 프로세스 방식 등을 비교하여 살펴보고 CPFR 프로세스를 연구한 후 CPFR 구현 사례 및 시사점을 연구하여 프로세스 공유를 통만 협력적 전자상거래의 활성화에 기여하고자 한다.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.309-321
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• 2014

Recently, due to the advent of ubiquitous computing and the spread of intelligent portable device such as smart phone, iPad and PDA has been amplified, a variety of services and the amount of information has also increased fastly. It is becoming a part of our common life style that the demands for enjoying the wireless internet are increasing anytime or anyplace without any restriction of time and place. And also, the demands for e-commerce and many different items on e-commerce and interesting of associated items are increasing. Existing collaborative filtering (CF), explicit method, can not only reflect exact attributes of item, but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, using a implicit method without onerous question and answer to the users, not used user's profile for rating to reduce customers' searching effort to find out the items with high purchasability, it is necessary for us to analyse the segmentation of customer and item based on customer data and purchase history data, which is able to reflect the attributes of the item in order to improve the accuracy of recommendation. We propose the method of recommendation system using association rule and weighted preference so as to consider many different items on e-commerce and to refect the profit/weight/importance of attributed of a item. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• International Commerce and Information Review
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• v.18 no.2
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• pp.131-150
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• 2016

As global partnership is recently getting important in order to establish supply chain network in global managing environment, companies tend to improve quality and warranty in SCM(Supply Chain Management) by certifying ISO(International Organization for Standardization). This research investigated structural relationship how strategic and operational ISO application level and trust, immersion, collaborative factors in partnership process affect to companies business performance. This research was surveyed with 147 manufacturing companies in South Korea and conducted empirical analysis using Structural equation model. The research hypothesis and model was derived from the basis of existing theory and empirical research, and obtained the following results. Firstly, the strategic ISO level showed positive(+) effect on trust and immersion factor. Second, the operational ISO level showed positive(+) effect on trust and collaborative factor. Third, trust factor in the partnership process showed positive(+) effect on immersion factor. Lastly, collaborative factor in partnership process showed positive(+) effect on companies business performance. Implication for this research is that companies must consider ISO application in establishing partnership and companies require prolonged effort using trust, immersing, collaborative factors into partnership process to improve business performance.

• The KIPS Transactions:PartD
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• v.9D no.2
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• pp.329-336
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• 2002

With the fast development of Internet environment, e-Commerce is rapidly increasing. n the expanding e-Commerce environment, the need for a new e-Commerce systems what will deliver products to the customer rapidly and increase sales is growing bigger. Recently, these requirements brought many researches on recommender systems. However, until now, those recommender systems have a limit because it takes too much time for recommender systems to give customers the recommendations in real time, if the number of purchase data of customers is large. So this paper concerns on the recommender systems using collaborative filtering as one of the solutions to increase the competitive power. We proposed and experimented the more improved recommender systems which could decrease the data size to shorten the recommending time by using the representative category of the product which customers want to buy. Also, we design and implement a recommender system using Enterprise JavaBeans.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.901-911
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• 2005

With the development of e-commerce and the proliferation of easily accessible information, information filtering has become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. While many collaborative filtering systems have succeeded in capturing the similarities among users or items based on ratings to provide good recommendations, there are still some challenges for them to be more efficient, especially the user bias problem, non-transitive association problem and cold start problem. Those three problems impede us to capture more accurate similarities among users or items. In this paper, we provide probabilistic model approaches for UCHM and ICHM which are suggested to solve the addressed problems in hopes of achieving better performance. In this probabilistic model, objects (users or items) are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. Experiments on a real-word data set illustrate that our proposed approach is comparable with others.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.715-721
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• 2018

With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.