• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2004년도 추계학술대회
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• pp.211-220
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• 2004

This paper suggests a collaborative business process between the companies that each has a restricted physical branch in its own area and wants to extend globally sales and delivery service. The companies integrate their business processes for sales and delivery using a shared product taxonomy table. We also suggest a similar product finding algorithm to make the product taxonomy table that defines product relationships to exchange them between the companies. The main idea of the proposed algorithm is using a multi-attribute decision making (MADM) to find the utility values of products in a same product class of the companies. Using the values we determine what products are similar. It helps the product manager to register the similar products into a same product sub-category. The companies then allow consumer to shop and purchase the products at their own residence site and deliver them or similar products to another sites.

• Asia pacific journal of information systems
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• 제15권1호
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• pp.63-79
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• 2005

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 system, WebCF-PT based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of traditional 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. A prototype recommendation system, WebCF-PT is developed and Internet shopping mall, EBIB(e-Business & Intelligence Business) is constructed to test the WebCF-PT system.

• 산업경영시스템학회지
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• 제36권2호
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• pp.74-80
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• 2013

In this study, a novel and flexible recommender system was developed, based on product taxonomy and usage patterns of users. The proposed system consists of the following four steps : (i) estimation of the product-preference matrix, (ii) construction of the product-preference matrix, (iii) estimation of the popularity and similarity levels for sought-after products, and (iv) recommendation of a products for the user. The product-preference matrix for each user is estimated through a linear combination of clicks, basket placements, and purchase statuses. Then the preference matrix of a particular genre is constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product with respect to the total. The popularity and similarity levels of a user's clicked product are estimated with an entropy index. Based on this information, collaborative and content-based filtering is used to recommend a product to the user. To assess the effectiveness of the proposed approach, an empirical study was conducted by constructing an experimental e-commerce site. Our results clearly showed that the proposed hybrid method is superior to conventional methods.

• 한국전자거래학회지
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• 제9권4호
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• pp.19-36
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• 2004

본 연구는 제품의 구매자와 수령자가 상이한 글로벌 협업 전자상거래를 구현하기 위한 비즈니스 모형 및 절차를 제안한다. 글로벌 협업 전자상거래를 수행하고자 하는 기업은 해당 기업의 소재 지역에만 지점을 보유하고 있으며 판매 및 물류 프로세스 협업을 통해 글로벌 판매를 수행하고자 한다. 해당 기업들은 교환 가능한 제품분류표를 공유함으로 인해 글로벌 협업 전자상거래를 수행할 수 있다. 이를 위해서는 기업들이 보유하고 있는 판매 제품과 교환판매가 가능한 제휴 기업의 제품을 찾아내기 위한 알고리즘이 필요하다. 본 연구에서는 제휴 기업간 교환 판매를 위해 교환 가능한 제품들을 찾아내기 위한 유사상품발견 알고리즘을 제안한다. 이 알고리즘은 제휴 기업들의 제품분류표에서 동일한 제품 군에 속해있는 제품들의 사양 값에 따라 효용 값을 계산하는 다기준 의사결정기법에 근거한다. 제안된 알고리즘에 근거한 글로벌 협업 전자상거래 모형을 활용하게 되면 기업들은 물리적 지점의 확장 없이 글로벌 판매를 수행할 수 있으며 고객은 자신의 거주 지역에서 구매한 제품을 다른 지역으로 저렴하게 배달시킬 수 있게 될 것이다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권9호
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• pp.363-372
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• 2019

시퀀스란 두 항목 간의 순서가 존재하는 데이터를 말하며, 고객 한 명이 구매한 상품들이 나열된 구매이력 데이터는 대표적인 시퀀스 데이터 중 하나이다. 일반적으로 모든 상품은 대분류/ 중분류/ 소분류와 같은 상품 분류 체계를 가지며, 서로 다른 상품이더라도 비슷하다면 그 특성에 따라 동일한 범주로 분류된다. 따라서 본 논문에서는 두 구매이력 시퀀스 비교 시 상품의 구매 순서를 고려할 뿐만 아니라, 비교하고자 하는 두 상품이 다르더라도 서로 동일한 상품 군에 속한다면 더 높은 유사도를 부여하여 계산한다. 특히 구매이력 시퀀스 유사도 계산 성능에 직접적인 영향을 미치는 시퀀스 유사도 측정 방법을 선택하기 위해 본 연구에서는 대표적인 시퀀스 간 유사도 측정 방법인 레벤슈타인 거리, 동적 타임 워핑 거리, 니들만-브니쉬 유사도의 성능을 비교하였으며, 항목간의 계층구조도 반영하여 계산하도록 확장하였다. 기존의 유사도 측정 방법의 경우 시퀀스 내 상품 비교 시 상품의 일치 유무에 따라 단순히 0 또는 1의 값을 부여하여 계산한다. 하지만 제안 방법의 경우 서로 다른 상품이더라도 두 상품 간의 연관정도를 다르게 부여하기 위하여 상품 분류 트리를 사용하여 0에서 1 사이의 값을 가지도록 세분화하였다. 실험을 통해 세 알고리즘에 제안 방법을 적용한 경우 기존 방법에 비하여 구매이력 시퀀스 간의 유사도를 더 정확히 측정함을 확인하였다. 또한 정확성 측정 비교 실험을 통해 동적 타임 워핑 유사도가 다른 두 유사도 측정 방법에 비하여 시퀀스 내 상품의 연관 정도를 고려할 뿐만 아니라 두 시퀀스의 길이가 다른 경우에도 좋은 성능을 보였기 때문에 구매이력 데이터에서 시퀀스 간의 유사도 비교 시 가장 적합한 측정 방법임을 확인하였다.

• Journal of Information Processing Systems
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• 제17권1호
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• pp.107-123
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• 2021

In an online shopping site or offline store, products purchased by each customer over time form the purchase history of the customer. Also, in most retailers, products have a product taxonomy, which represents a hierarchical classification of products. Considering the product taxonomy, the lower the level of the category to which two products both belong, the more similar the two products. However, there has been little work on similarity measures for sequences considering a hierarchical classification of elements. In this paper, we propose new similarity measures for purchase histories considering not only the purchase order of products but also the hierarchical classification of products. Unlike the existing methods, where the similarity between two elements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposed method can assign any real number between 0 and 1 considering the hierarchical classification of elements. We apply this idea to extend three existing representative similarity measures for sequences. We also propose an efficient computation method for the proposed similarity measures. Through various experiments, we show that the proposed method can measure the similarity between purchase histories very effectively and efficiently.

• Asia pacific journal of information systems
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• 제9권1호
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• pp.17-38
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• 1999

While the value of information technology has long been a hot issue, few solid results have been found as of yet. It is partly due to methodological factors and model underspecifcation. This study empirically develops a ITS(information technology structure) taxonomy and investigates the relationships between ITS taxonomy and business performance in the Korean firms. Among factors that impact business performance, organization structure and control system are selected and they are hypothesized to moderate-the relationships between ITS taxonomy and business performance. By surveying 91 manufacturing firms and applying hierarchical cluster analysis, four ITS are identified : centralized, decentralized, centralized cooperative, decentralized cooperative. ANOVA, correlation analysis and crosstable analysis say the presence of moderating effect of organization structure and control system. Cooperative ITS is best in business performance. Centralized ITS is related to functional organizational form. Decentralized ITS is related to product organizational form with decentralized decision making, Centralized cooperative ITS is related to matrix organizational form. Decentralized cooperative ITS is related to matrix organizational form with high integration. These findings have implications for the opportunities and challenges to match information technology with organization structure and control system.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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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.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.312-319
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• 2002

A recommendation system tracks past action of a group of users to make a recommendation to individual members of the group. The computer-mediated marking and commerce have grown rapidly nowadays so the concerns about various recommendation procedure are increasing. We introduce a recommendation methodology by which Korean department store suggests products and services to their customers. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is to select target customers, who have high purchase possibility of recommended products. Product taxonomy and association rule mining are used to select proper products. The validity of our recommendation methodology is discussed with the analysis of a real Korean department store.

• 대한산업공학회지
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• 제29권3호
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• pp.230-238
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• 2003

The configuration design of a mechanical product can be efficiently performed when it is based on the functional modeling. There are methodologies, which decompose function from the abstract level to the concrete level and match the functions to physical parts. But it is difficult to carry out an innovative design when the function is matched only to a pre-detined part. This paper describes the configuration design process of a mechanical product with a design expert system, which uses function taxonomy and TRIZ theory. The expert system can propose a functional modeling of a new part. which is not in the existing parts list. The abstraction levels of design knowledge are introduced, which describe the operation of mechanical product in the levels of abstraction. This is the theoretical background of using knowledge of function and TRIZ for configuration design. The expert system is adequate to control this design knowledge. which expresses knowledge of functional modeling, mapping rules between functions and parts, selection of parts, and TRIZ theory. The hierarchy of functions and machine parts are properly expressed by classes and objects in the expert system. A design expert system has been implemented for the configuration design of a train bogie, and a new brake system of the bogie is introduced with the aid of TRIZ's 30 function groups.