• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.265-268
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• 2001

As growing the electronic commerce there are significant changes in the products/services catalog into the on-line environment. Advertent of e-catalog business opportunity for their own product/services enlarges the market volume and there are diverse methods for the presentation of its product/services. A method for the presentation of product/services features one uses identification and classification system. This study constructs a classification system and database layout for the product/services classification system as a part of e-catalog system. We consider the specific method for the GDAS-based dataset and UNSPSC classification system in the distribution industry.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.2
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• pp.287-304
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• 2016

This study examines the influence of product classification standards and structure on user perception as well as their attitude towards online shopping sites. The causal relationships of variables are also examined. The analysis was based on an online survey with 247 responses. Four types of internet shopping sites were developed and used as a stimulus. The results of the mean comparison analysis indicated that perceived variety, information overload, perceived shopping value and attitude towards the site varies significantly with product classification standards and structure. There was also of a marginally significant interaction between the classification standard and structure on perceived variety and information overload. The causal relationship analysis revealed that perceived variety positively influenced hedonic and utilitarian shopping value. However, information overload had a negative effect on hedonic and utilitarian shopping value. Both the hedonic and utilitarian shopping value positively influenced attitudes towards the sites. This study demonstrates that classification method influences customer perception and attitude. It offers interesting insights on a product classification method as a strategic tool for online shopping.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.67
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• pp.77-82
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• 2001

A study was conducted to develop a better classification method of Consumer Attributes that can enhance user-centered product design process. A modified QFD(Quality Function Deployment) survey form based upon Fuzzy set theory was proposed which contains 9 steps of importance level, and Certainty and Necessity function to improve the reliability of extracted consumer attributes. To verify the betterment and advantage of proposed classification method, a series of questionnaire survey was performed. Thirty male and 30 female university students were participated in the survey using a VCR as a target product. The result of the study showed that 80% of subjects were preferred the proposed classification over existing method. A cluster analysis was performed to further verify the betterment of the proposed method. The result also supported that the proposed classification method is more reliable and enhanced method in extracting consumer attributes and can be applied in the product design.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.119-131
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• 2019

Among the themes for CSR(Corporate Social Responsibility), consumer issues include protecting the health and safety of consumers who purchase and use the products. In particular, ensuring product safety is a major theme of consumer issues for corporate social responsibility. Currently, the government implements the Electrical Appliances and Consumer Products Safety Control Act for product safety management and selects products that may harmful to consumers as safety control items, and manages the products by designating them as 4 types of safety certification, safety confirmation, supplier conformity verification, and safety standard compliance. In this paper, we propose management plans for the establishment of a more reasonable classification type of safety management target for 48 items of consumer products to be controlled by the act, and confirm the validity of the plan. First, we perform cluster analysis using data for CISS (Consumer Injury Surveillance System) to derive a new classification type of the safety management target. Next, we compare the results of the cluster analysis with the classification type of the act and the existing scenario classification method RAS (Risk Assessment by Scenario) and the causal network method RAMP (Risk Assessment Method based on Probability). Based on these results, we propose two new plans of safety management target classification and verify its validity.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.254-262
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• 2009

As the trend of e-business activities develop, customers come into contact with products through on-line shopping sites and lots of customers refer product reviews before the purchasing on-line. However, as the volume of product reviews grow, it takes a great deal of time and effort for customers to read and evaluate voluminous product reviews. Lately, attention is being paid to Opinion Mining(OM) as one of the effective solutions to this problem. In this paper, we propose an efficient method for opinion sentiment classification of product reviews using product specific context information of words occurred in the reviews. We define the context information of words and propose the application of context for sentiment classification and we show the performance of our method through the experiments. Additionally, in case of word corpus construction, we propose the method to construct word corpus automatically using the review texts and review scores in order to prevent traditional manual process. In consequence, we can easily get exact sentiment polarities of opinion words in product reviews.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.109-118
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• 2007

This paper proposed a method that can be simply analyzed instead of the basic general Fuzzy rule that its insufficient characters are cut out. Based on the proposed method. Rough sets are used to eliminate the incomplete attributes included in the rule and also for a classification more precise; the agreement of the membership function's output extracted the maximum attributes. Besides, the proposed method in the simulation shows that in order to verify the validity, compare the max-product result of fuzzy before and after reducing rule hosed on the rice taste data; then, we can see that both the max-product result of fuzzy before and after reducing rule are exactly the same; for a verification more objective, we compared the defuzzificated real number section.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.128-135
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• 2018

Currently, the government selects products that are likely to cause harm to consumers as safety management targets and classifies them into three types: safety certification, safety confirmation, and supplier conformity verification. In addition, the government conducts safety surveys on products in circulation or accident products, and recalls products that are of great concern to consumer risks. In this paper, we have developed RAP (Risk Assessment method based on Probability), which is a probability based product risk assessment method, for the classification of safety management type of product and safety investigation, and have shown an application example. In this process, information is used for the CISS (Consumer Injury Surveillance System) of the Korean Consumer Agency. In addition, we apply the cluster analysis to classify the current supervised children products into three groups. Then, we confirm the effectiveness of RAP by comparing the result of RAP application, cluster analysis result and current safety management classification type. Also, we recognize the need to review the current safety management classification criteria for classifying products into three types.

• 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.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.491-500
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• 2007

In order to classify and manage on-line trading goods, the product classification scheme must be maintained. In most systems for handling product information, the classification scheme is managed manually by experts, which in general incurs a lot of time and cost. Effective management of classification system becomes more important as rapid development of industry expedites diversity and convergence of goods and services. There have been many researches on developing classification scheme, and continuing in this line of research, this paper proposes a new method for automatic generation of product classification scheme. Our main idea starts from the concept that a product is a set of attributes, and we propose a novel algorithm for automatically creating hierarchical classification scheme by utilizing inclusive relationships between products. We then prove the effectiveness of proposed algorithm by conducting an experiment.

• Journal of Computational Design and Engineering
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• v.3 no.3
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• pp.274-285
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• 2016

A designer is mainly supported by two essential factors in design decisions. These two factors are intelligence and experience aiding the designer by predicting the interconnection between the required design parameters. Through classification of product data and similarity recognition between new and existing designs, it is partially possible to replace the required experience for an inexperienced designer. Given this context, the current paper addresses a framework for recognition and flexible retrieval of similar models in product design. The idea is to establish an infrastructure for transferring design as well as the required PLM (Product Lifecycle Management) know-how to the design phase of product development in order to reduce the design time. Furthermore, such a method can be applied as a brainstorming method for a new and creative product development as well. The proposed framework has been tested and benchmarked while showing promising results.