• Journal of Information Technology Applications and Management
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• 제23권4호
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• pp.55-70
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• 2016

Recently, development of indoor positioning system and IoT such as beacon makes it possible to collect and analyze each customer's shopping behavior in off-line shopping malls. In this study, we propose a realtime brand recommendation scheme based on each customer's brand visiting history for off-line shopping mall with indoor positioning system. The proposed scheme, which apply collaborative filtering to off-line shopping mall, is composed of training and apply process. The training process is designed to make the base brand network (BBN) using historical transaction data. Then, the scheme yields recommended brands for shopping customers based on their behaviors and BBN in the apply process. In order to verify the performance of the proposed scheme, simulation was conducted using purchase history data from a department store in Korea. Then, the results was compared to the previous scheme. Experimental results showd that the proposed scheme performs brand recommendation effectively in off-line shopping mall.

• 건축역사연구
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• 제22권5호
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• pp.37-46
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• 2013

This paper aims to examine the constructional background and process of the Japanese military installations of Jisim-do, especially based on the military secret documents. Furthermore, it aims to analyze the characteristics of the remains. First, the study looked into the procedure of forcible occupation by Japan, involving the background of the designation and forcible accommodation of military reservations, and forced eviction by the purchase of land. Second, the study identified the background of construction, purpose, and construction period of each battery built throughout the 'Fort maintenance period' according to changes in international situations. Third, it is the 'Chukseongbu' that supervised the construction of fortresses. Fourth, the study considered a series of arrangement processes in which Jisim-do became a fortresses through "Yukgunsungdae-ilgi", a military operations report for the Japanese army. Through this, it discovered a clear construction process, construction details, and the supply for Jisim-do. The study was also able to reveal the meticulousness in constructing firm facilities more promptly from the 'design tactics'.

• Journal of Information Technology Applications and Management
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• 제20권3_spc호
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• pp.219-230
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• 2013

Real time accessiblity and agility in Ubiquitous-commerce is required under ubiquitous computing environment. The Research has been actively processed in e-commerce so as to improve the accuracy of recommendation. Existing Collaborative filtering (CF) can not reflect contents of the items and has the problem of the process of selection in the neighborhood user group and the problems of sparsity and scalability as well. Although a system has been practically used to improve these defects, it still does not reflect attributes of the item. In this paper, to solve this problem, We can use a implicit method which is used by customer's data and purchase history data. We propose a new clustering method of weighted preference for customer using k-means clustering and Bayesian network in order to improve the accuracy of recommendation. To verify improved performance of the proposed system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• 대한산업공학회지
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• 제41권1호
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• pp.10-16
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• 2015

In this study, we suggest a method to predict probability distribution of a new customer's degree of loyalty using C-CRF that reflects the RFM score and similarity to the neighbors of the customer. An RFM score prediction model is introduced to construct the first feature function of C-CRF. Integrating demographical similarity, purchasing characteristic similarity and purchase history similarity, we make a unified similarity variable to configure the second feature function of C-CRF. Then parameters of each feature function are estimated and we train our C-CRF model by training data set and suggest a probabilistic distribution to estimate a new customer's degree of loyalty. An example is provided to illustrate our model.

• 한국컴퓨터정보학회논문지
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• 제21권7호
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• pp.9-16
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• 2016

Collaborative filtering(CF) uses the purchase or item rating history of other users, but does not need additional properties or attributes of users and items. Hence CF is known th be the most successful recommendation technology. But conventional CF approach has some significant weakness, such as the new user problem. In this paper, we propose a approach using word embedding with skip-gram for learning distributed item representations. In particular, we show that this approach can be used to capture precise item for solving the "new user problem." The proposed approach has been tested on the Movielens databases. We compare the performance of the user based CF, item based CF and our approach by observing the change of recommendation results according to the different number of item rating information. The experimental results shows the improvement in our approach in measuring the precision applied to new user problem situations.

• 한국도로학회논문집
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• 제14권5호
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• pp.157-164
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• 2012

PURPOSES: Regional offices of the Ministry of Land, Transport and Maritime Affairs use a computerized system called KAMIS so as to manage road equipment systematically. Road agencies can record number of operating days by equipment, actual working hours, accumulated operating hours (or distance) by equipment, and operating cost. However, KAMIS does not provide critical information, although it is strongly related to efficient road management equipment operation. In other words, road agencies do not know whether they have sufficient equipment to handle their actual work. METHODS: Therefore, this study suggests a methodology to evaluate for operational efficiency of road management equipment using analytical hierarchy process(AHP). First of all, estimated weights related criteria can be produced by AHP, and then use operational history by pieces of equipment. RESULTS: Results show that importance of management work can differ from weather conditions through five areas. CONCLUSIONS: Commonly, this results can imply to help save money for the purchase and maintenance of road management equipment, and they would improve the functional performance of KAMIS.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.93-97
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• 2002

In this paper, we propose personalized recommendation techniques based on multidimensional scaling (MDS) method for Business to Consumer Electronic Commerce. The multidimensional scaling method is traditionally used in marketing domain for analyzing customers' perceptional differences about brands and products. In this study, using purchase history data, customers in learning dataset are assigned to specific product categories, and after then using MDS a positioning map is generated to map product categories and alternative advertisements. The positioning map will be used to select personalized advertisement in real time situation. In this paper, we suggest the detail design of personalized recommendation method using MDS and compare with other approaches (random approach, collaborative filtering, and TOP3 approach)

• Asia pacific journal of information systems
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• 제9권4호
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• pp.107-124
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• 1999

This paper describes a personalized advertisement technique as a part of intelligent customer services in Internet shopping malls. Based on customers' initial profile, purchase history, and behaviors in an Internet shopping mall, the technique displays appropriate advertisements on Internet web pages when customers' visit to the shopping mall. Customers preference scores for product groups which are main sources to select advertisements, are stored either a preference table or preference trees. Both of the two storage methods can support selection of advertisements on real time, and the preference tree method can reflect affinity among product groups. The suggested technique selects different advertisements to reflect changes of customers preferences as time goes by. An experiment has been performed to evaluate the effectiveness of the algorithm, which revealed that the algorithm selects more customer-oriented advertisements rather than random selection.

• 인터넷정보학회논문지
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• 제21권2호
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• pp.65-72
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• 2020

본 논문에서는 사용자 식별용 분산 아이디(DID, Distributed ID)를 적용한 블록체인의 분산 노드(Node)로서 개인인증을 위한 USB 지문인식 모듈을 설계 하였다. 생체 연계형 지문인식 모듈은 FIDO(Fast IDentity Online) 서버가 거래인증을 확인하기 위한 실시간 과정을 온라인 인증 웹 사이트에서 검증한다. 이로서 블록체인 분산ID 기반의 거래인증을 확인하기 위해 스마트 디바이스와 연동하는 개인별 시청률 조사 방안 및 맞춤형 쇼핑몰에서 구매예정 상품과 가상화폐를 추천할 수 있다. DID를 기반으로 한 개인 사용자 식별을 통한 채널의 변경정보를 인식함으로서, 시청률 조사가 신뢰성을 향상 할 수 있게 된다. 이러한 분산 아이디를 활용한 온라인 쇼핑 몰에서 상품구매 정보이력을 활용할 수 있다. 이로서 구매를 위한 상품정보를 블록체인으로 공유함으로서, DID기반의 맞춤형 쇼핑 몰 추천 방식을 제공할 수 있다. 또한, 블록체인 FIDO 서비스는 지문/안면 인증과 같은 기법을 통해 블록체인 노드로서 삼성 S10 단말의 키스토어(Key-srore) 인증 이외에도, 부가적인 거래의 인증을 활용할 수 있게 된다.

• 경영과정보연구
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• 제39권2호
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• pp.113-126
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• 2020

본 연구는 브랜드원산지의 개념을 국가이미지, 제조능력이미지로 구분하여 이들 변수들의 브랜드신뢰 및 브랜드충성도에 대한 직접적인 영향을 검증하였으며 이에서 검증된 결과인 브랜드 원산지의 브랜드신뢰에 미치는 영향을 가지고 제품별 차이점을 검증하는 것으로 논문을 마무리 하였다. 본 논문에서 나타난 구체적 시사점은 다음과 같다. 첫째, 본 연구는 브랜드원산지는 브랜드충성도에 직접적인 영향을 미치지 못하지만 브랜드충성도의 선행변수들에는 충분히 직접적 영향을 미칠 수 있다는 것을 제시하고 있다. 소비자의 제품구매나 선택에 직접적 영향을 미치기 보다는 제품 속성에 대한 평가나 브랜드 이미지에 대한 긍정적 영향을 통해 선택에는 간접적 영향을 미치는 요소로 정의하는 것이 바람직하다고 결론내릴 수 있다. 둘째, 제품특성별 브랜드 원산지 영향력의 차이는 매우 뚜렷하게 나타났다. 과거 연구들은 몇 가지 제품에 국한되어 범 제품적 테스트가 이루어지지 못하였고 그 실증력의 한계가 있다고 판단되어 본 연구에서는 다양한 제품별을 포함시키고 실제 실증적 연구를 통하여 제품별 차이를 발견하려고 했다. 관여도와 자아일치성은 브랜드원산지가 브랜드성과 변수에 영향을 미치는데 있어 매우 중요한 조절변수로 판단할 수 있는 결과가 제시되었다. 또한, 품질, 역사, 진정성 등에 관련된 인식 고저나 유무로 제품을 구분하여 제품특성을 구분하여 각 제품특성별 브랜드원산지의 역할을 본 결과 품질이 높다고 인식된 제품이 그리고 진실성이 높다고 인식된 제품이 브랜드원산지의 효과가 더 높은 것으로 나타났으나 역사는 브랜드원산지의 효과를 보여주지 못한 제품특성이었다.