• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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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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• v.15 no.1
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.2
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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.

• The Journal of Society for e-Business Studies
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• v.9 no.4
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• pp.19-36
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• 2004

This paper suggests a business process between the collaborative companies that want to extend globally sales and delivery service with restricted physical branches in their own areas. The companies integrate their business processes for sales and delivery services using a shared product taxonomy table. In order to perform the collaborative processes, they need the algorithm to exchange their own products. We suggest a similar product finding algorithm to compose 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. Based on 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.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.363-372
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• 2019

A sequence refers to data in which the order exists on the two items, and purchase transaction data in which the products purchased by one customer are listed is one of the representative sequence data. In general, all goods have a product taxonomy, such as category/ sub-category/ sub-sub category, and if they are similar to each other, they are classified into the same category according to their characteristics. Therefore, in this paper, we not only consider the purchase order of products to compare two purchase transaction sequences, but also calculate their similarity by giving a higher score if they are in the same category in spite of their difference. Especially, in order to choose the best similarity measure that directly affects the calculation performance of the purchase transaction sequences, we have compared the performance of three representative similarity measures, the Levenshtein distance, dynamic time warping distance, and the Needleman-Wunsch similarity. We have extended the existing methods to take into account the product taxonomy. For conventional similarity measures, the comparison of goods in two sequences is calculated by simply assigning a value of 0 or 1 according to whether or not the product is matched. However, the proposed method is subdivided to have a value between 0 and 1 using the product taxonomy tree to give a different degree of relevance between the two products, even if they are different products. Through experiments, we have confirmed that the proposed method was measured the similarity more accurately than the previous method. Furthermore, we have confirmed that dynamic time warping distance was the most suitable measure because it considered the degree of association of the product in the sequence and showed good performance for two sequences with different lengths.

• Journal of Information Processing Systems
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• v.17 no.1
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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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• v.9 no.1
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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.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.3
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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.