• The Transactions of the Korea Information Processing Society
• /
• v.4 no.10
• /
• pp.2533-2543
• /
• 1997

It is necessary to understand and apply the concept of Quality of Service(QoS) for the objective selection among the computer network equipment. Because ITU-T E.800 recommendation covers the service quality of provider's viewpoint and the satisfaction of user, it can be used to evaluate and select the product of computer network systems. This paper is concerned with the development of an evaluation model using QoS and software tool for selection of the most suitable LAN switch. We apply the Analytic Hierarchy Process(AHP) method of Saaty which has been in a multiple criteria framework for an effective group decision process to the selection of LAN switch. The sample data are collected and processed from a questionnaire of professionals in the network field. And we implement a prototype tool for the selection of LAN switch according to the suggested selection model and analyse the result. The result of our research is expected to be a useful tool for decision making to evaluate and select the LAN switch and also can be applied to the decision making of evaluation and selection related to the product of computer network with QoS.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.27-42
• /
• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Culinary science and hospitality research
• /
• v.15 no.3
• /
• pp.248-261
• /
• 2009

The purpose of this study is to examine the effects of selection attribute in big discount stores upon post-purchase consumer behavior and provide reference materials required for big discount stores to develop customer satisfaction strategies. As a result, this study shows the following findings: First, product-related factors had positive effects on post-purchase consumer behavior. Second, service-related factors had positive effects on post-purchase consumer behavior. Third, store-related factors had positive effects on post-purchase consumer behavior. Conclusively, it is advisable that big discount stores provide a variety of personalized services for customers to create and attract their trust, motivating effective recommendation to their acquaintances.

• Proceedings of the KAIS Fall Conference
• /
• 2003.11a
• /
• pp.118-126
• /
• 2003

In data mining, having access to large amount of data sets for the purpose of predictive data does not guarantee good method, even where the size of Real data is Mobile commerce unlimited. In addition to searching expected Goods objects for Users, it becomes necessary to develop a recommendation service based on XML. In this paper, we design the optimized XML Recommender product data. Efficient XML data preprocessing is required, include of formatting, structural, and attribute representation with dependent on User Profile Information. Our goal is to find a relationship among user interested products from E-Commerce and M-Commerce to XDB. Firstly, analyzing user profiles information. In the result creating clusters with analyzed user profile such as with set of sex, age, job. Secondly, it is clustering XML data which are associative products classify from user profile in shopping mall. Thirdly, after composing categories and goods data in which associative objects exist from the first clustering, it represent categories and goods in shopping mall and optimized clustering XML data which are personalized products. The proposed personalized user profile clustering method has been designed and simulated to demonstrate it's efficient.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2000.10a
• /
• pp.313-316
• /
• 2000

대부분의 온라인 전자상거래에서 상품 추천 서비스는 사용자의 정보 또는 구매 이력을 가지고 카테고리를 중심으로 상품을 추출하여 추천을 하는 구조이다. 또, 카테고리를 중심으로 추천을 하다 보니 단일한 구매 패턴에 의해서만 추천을 하게 되고, 상품에 각각에 대한 연관성을 찾아보기 힘들다. 또 단일 구매 패턴은 계산 비용이 작기는 하지만 사용자의 구매 패턴을 정확하게 반영하기 어렵다. 본 논문에서는 이러한 문제를 해결하기 위하여 카테고리 독립적이고, 다중 구매패턴을 고려한 상품추천 서비스의 설계를 제안한다 이를 위하여 단일 항목간의 구조화를 통하여 항목간의 연계성을 고려한 구조를 설계한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.07a
• /
• pp.705-708
• /
• 2023

본 논문은 사용자의 피부 상태에 따라 사용자에게 적합한 화장품을 소개해주는 화장품 추천 웹 쇼핑몰, "PBTI"를 개발한다. 요즘 유행하는 성격 유형 설문조사인 MBTI에서 영감을 받아 피부 유형과 퍼스널 컬러를 검사하고 이를 기반으로 화장품을 추천하는 온라인 쇼핑몰 웹사이트를 제작하게 되었다. 바우만 교수의 피부 유형 지표를 바탕으로 제작된 질문을 통해 사용자들의 피부 유형을 검사하고 해당 피부 유형 결과에 따른 상품을 추천해주는 알고리즘이 탑재되어 사용자에게 맞는 상품을 추천해준다. 텐서플로우 기반의 인공지능을 탑재하여 퍼스널컬러 테스트를 제작하였다. PBTI의 이러한 무료 테스트 서비스 제공은 다른 온라인 뷰티 쇼핑몰과 극명한 차별점을 만들고, 쇼핑몰 매출을 크게 증대시킬 것으로 기대한다.

• The Korean Journal of Franchise Management
• /
• v.10 no.2
• /
• pp.19-32
• /
• 2019

Purpose - This paper aims to investigate the mediating effects of relationship fairness factors between franchisor's support services and performance(re-contract intention) in food service franchise. More specifically, fairness was measured into distributive, procedure, interaction, and information, franchisor's support service was divided into pre-start support services (initial support services) and post-start support services (continued support services), and performance (re-contract intent) was measured using 3 items such as re-contract, contract extension, and recommendation. Research design, data, and methodology - The population for the survey is the head of franchises in the metropolitan area (Seoul/Gyeonggi), which operates a restaurant franchise, and samples included a wide range of overseas/domestic brands and regions. The survey was conducted from August 1 to September 30, 2018 through the survey agency. The survey was conducted together with a telephone interview and a direct visit by the investigator. A total of 205 questionnaires were collected and retrieved, 4 questionaires containing missing information were excluded and 201 responses were used for analysis. Results - The results shows that franchisor's initial support services have significant positive effects on procedural, interpersonal, and informational relationship fairness, and continuous support services have significant positive effects on distributive, procedural, interpersonal, and informational relationship fairness. This study also shows that informational and procedural fairness have significant positive effects on performance(re-contract intention). Finally, continuous services a significant positive effect on performance(re-contract intention). Conclusions - The results show that franchisor make a manual, and should improve fairness through regular investigation whether support services was executed as promised in the manual after franchisee operation. In addition, information fairness and procedural fairness have been shown to increase performance(re-contract intention). These results mean that the franchisor's headquarters should provide product and service support for the merchant in accordance with the manual and management policy to reduce asymmetry in information and improve procedural fairness to enhance performance(re-contract intention).

• The Journal of Industrial Distribution & Business
• /
• v.9 no.12
• /
• pp.31-43
• /
• 2018

Purpose - Smartphone shopping malls provide customers with a variety of tangible and intangible services including web sites, web design, use convenience, information for products and shopping and various after services. Accordingly, it is needed to expand and classify service qualities based on the various services provided by smartphone shopping malls, and then analyze path structures of smartphone shopping malls' qualities → customer satisfaction → behavioral intention. The purpose of this study is to categorize the qualities of smartphone shopping mall users based on the e-SERVQUAL by Lee(2002) and the SERVQUAL by Parasuraman et al.(1988, 2005), the smartphone shopping malls' service qualities based on service quality of smartphone shopping malls used in the previous use studies, and the Website quality factors of service industry and to analyze path structure of smartphone shopping mall's qualities → customer satisfaction → behavioral intention on college students in order to confirm the system of smartphone shopping malls' qualities. Research design, data, and methodology - This study's survey was carried out on the college students of university located in northeastern of Seoul. It was from December 7 - 15, 2017, and a total of 240 questionnaires were distributed, with 228 collected. Of them, effective questionnaires used in the final study were a total of 201 except 27 that couldn't be used. In this study, empirical analysis was done with factor analysis, correlation analysis, multiple regression analysis, simple multiple regression analysis and moderating regression analysis by using Statistics Package SPSS18.0. Results - The study results are as follows: First, smartphone shopping malls' qualities were classified into six categories like customer system quality, Web design quality, convenience quality, information-offering quality, service quality, and product quality. Second, it showed that system quality, Web design quality, and information-offering quality had a positive impact on customer satisfaction, respectively. Third, it suggested that quality factors of smartphone shopping mall users had a positive impact on customer satisfaction in the order of quality, information-offering quality, system quality and Web design quality. Finally, it showed that customer service quality, product quality, and convenience quality did not have a positive impact on customer satisfaction. In addition, it said that customer satisfaction of smartphone shopping mall users had a positive impact on behavioral intention and thereby, the higher the customer satisfaction was, the higher the relations between reuse intention and recommendation intention were. Meanwhile, moderating regression analysis showed that trust did not have moderating effect in the relations between customer satisfaction and behavioral intention. The above study revealed that smartphone shopping malls' qualities were classified into six categories and it was possible to generalize after empirical analysis was made in the path structure. Conclusions - Smartphone shopping mall users consider usefulness of obtaining shopping information and quality on quick and abundant shopping information more important than access environment of smartphone shopping malls and kind services of smartphone shopping mall managers. Thereby, smartphone shopping mall marketers need to take service qualities like system quality and information-offering quality into more consideration.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2003.05a
• /
• pp.319-328
• /
• 2003

With the proliferation of WWW, providing more intelligence to Web sites has become a major concern in e-business industry. In recent days, this trend is more accelerated by prosperity of CRM (Customer Relationship Management) in terms of various aspects such as product recommendation, self after service, etc. To accomplish this goal, many e-companies are eager to embed web enabled rule-based system, that is, expert systems into their Web sites and several well-known commercial tools are already available in the market. Most of those tools are developed based on CGI so far but CGI based systems inherently suffer over-burden problem when there are too many service demands at the same time due to the nature of CGI. To overcome this limitation of the existing CGI based expert systems, we propose a new form of Web-enabled expert system using hyperlink-based inference mechanism. In terms of burden to Web server, our approach is proven to outperform CGI based approach theoretically and also empirically. For practical purpose, our this approach is implemented in a software system, WeBIS and a graphic rule editing methodology, Expert Diagram is incorporated into the system to facilitates rule generation and maintenance. WeBIS is now successfully operated for financial consulting in the web site of a leading financial consulting company in Korea.

• Journal of Intelligence and Information Systems
• /
• v.17 no.1
• /
• pp.127-138
• /
• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.