• 대한설비관리학회지
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• 제23권4호
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• pp.57-64
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• 2018

User-based collaborative filtering is a method of recommending an item to a user based on the preference of the neighbor users who have similar purchasing history to the target user. User-based collaborative filtering is based on the fact that users are strongly influenced by the opinions of other users with similar interests. Item-based collaborative filtering is a method of recommending an item by comparing the similarity of the user's previously preferred items. In this study, we create a recommendation model using user-based collaborative filtering and item-based collaborative filtering with consumer's consumption data. Collaborative filtering is performed by using RFM (recency, frequency, and monetary) technique with purchasing data to recommend items with high purchase potential. We compared the performance of the recommendation system with the purchase amount and the performance when applying the RFM method. The performance of recommendation system using RFM technique is better.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2008년도 춘계 종합학술대회 논문집
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• pp.575-578
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• 2008

현재 대부분의 자동차 부품 제조업체에서는 자동차 부품개발 및 구매, 생산 관리 등에 어려움을 격고 있으며, 자동차 부품의 신규 개발 및 설계 변경 시 신규 등록 및 이력 관리에 어려움이 있다. 이에 따라 여러 가지 부품에 대한 자동화된 이력관리와 부품의 데이터베이스 구축에 대한 필요성을 절실히 느끼고 있는 상항이다. 이에 본 논문은 자동차 제조 부품관련 데이터 검색과 실시간 생산통합관리 시스템 개발을 위한 설계를 목적으로 한다. 설계 데이터 변경에 대한 자재변경과정을 입력함으로써 자동적으로 데이터 갱신이 이루어질 수 있도록 하였고, 부품에 대한 품명, 품번을 입력하면 관련 부품 이력정보의 통합관리가 가능하도록 하였다.

• 한국의상디자인학회지
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• 제20권3호
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• pp.27-32
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• 2018

Hanbok is a our own history and tradition and is an icon of Korean history and culture. Today, the costumes are moving around the world fashion trend due to the development of mass media and internet at the same time. This is an important clue to marketing activities and can be used for predictive analysis. Although Hanbok is changing little by little every year, research on the trend of Hanbok is rare. This study analyzed the results of searching for 'Hanbok' as a keyword in Portal Site Naver and posting a customized purchase of Hanbok for marriage between 2016 and 2017. The analysis was based on analyzing the photos uploaded, and analyzing purchase reason in the On-line review. Most buyer of Hanbok purchased for prepare marriage. The choice of a customized hanbok is mostly to search online or to use the fair. The most important factor in choosing a custom Hanbok that appeared in online reviews is color and then price. The color of the jacket is mostly light color and the off-white color is the most used and the long skirt such as the pink system, the chorale system and the red system, and it can be seen that the pink skirt is overwhelmingly large. In the design of Hanbok, The sleeves were straight and narrow, and the length was the chest line. The collar were enlarged and widened. 고름 used the sole color instead of the jacket and skirt color, and it was narrow not long. skirt's pleats was wide, and designed to overlap with double color of the fabric.

• 한국컴퓨터정보학회논문지
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• 제13권2호
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• pp.41-50
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• 2008

모바일 환경하에의 RFM 기법을 이용한 개인화 된 추천 시스템을 제안한다. 사용자의 평가 자료에 의존하지 않고 사용자에게 번거로운 질의 응답 과정이 없이 묵시적인(Implicity) 방법을 이용하여 고객정보와 구매이력정보를 기반으로 RFM 기법을 이용하여 고객 세분화와 아이템 세분화 통해서 대상 사용자에게 구매 가능성이 높은 아이템을 추천한다. 또한 기존의 추천시스템의 문제점의 해결 방안으로 신규 고객이나 신규 아이템 추천을 고려하여 적용한다. 추천 아이템과 사용자가 구매한 아이템 이력 데이터를 비교하여 추천된 아이템이 중복 추천을 제거하였고 현업에서 사용하는 데이터 셋을 구성하여 실험을 통해서 효용성과 타당성을 입증 및 평가하여 개인화된 일대일 웹 마케팅을 실현하였다.

• 한국인터넷방송통신학회논문지
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• 제18권6호
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• pp.269-276
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• 2018

OCR(광학문자인식)은 컴퓨터 분야에 적용된 지 20년의 역사가 되었고, 자동차 번호판 인식을 통한 주차관리 등 여러 분야에서 응용되어왔다. 본 OCR 기반 스마트 가계부 앱 개발연구에서도 이 기술을 이용하였다. 스마트폰 기반 가계부에서 구매 내역을 수기로 일일이 기입하는 불편을 개선하고자 카메라로 영수증을 촬영해서 구입 목록을 자동으로 정리할 수 있도록 하였다. 이 과정에서 기존의 OCR 기술만으로 영수증의 이미지 문자를 판독하면 인식률이 떨어져서 영상처리기술을 이용하여 영수증 사진의 대비를 조절하는 방법으로 영수증의 문자 인식률을 89%에서 92.5%로 향상하였다.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.752-770
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• 2019

The purpose of this study is to incorporate telemarketing processes to improve telemarketing performance. For this application, we have attempted to mix the model of machine learning to extract potential customers with personalisation techniques to derive recommended products from actual contact. Most of traditional recommendation systems were mainly in ways such as collaborative filtering, which predicts items with a high likelihood of future purchase, based on existing purchase transactions or preferences for products. But, under these systems, new users or items added to the system do not have sufficient information, and generally cause problems such as a cold start that can not obtain satisfactory recommendation items. Also, indiscriminate telemarketing attempts can backfire as they increase the dissatisfaction and fatigue of customers who do not want to be contacted. To this purpose, this study presented a multi-purpose hybrid recommendation algorithm to achieve two goals: to select customers with high possibility of contact, and to recommend products to selected customers. In addition, we used subscription data from telemarketing agency that handles insurance products to derive realistic applicability of the proposed recommendation system. Our proposed recommendation system would certainly solve the cold start and scarcity problem of existing recommendation algorithm by using contents information such as customer master information and telemarketing history. Also. the model could show excellent performance not only in terms of overall performance but also in terms of the recommendation success rate of the unpopular product.

• 농촌계획
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• 제28권4호
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• pp.83-91
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• 2022

The goal of this study is to promote the spread of environmentally friendly agricultural products, which have been receiving a lot of attention lately, as a way to improve food safety and quality. As a result of the increased labor input, increased production costs, and an aging population, environmentally friendly agriculture is making it more difficult for farmers to expand their enterprises. In the meantime, consumers find it difficult to spread eco-friendly agricultural products due to their high expectations for safety and quality, as well as the desire to purchase products at a reasonable price. Previous studies have revealed differences in perceptions between eco-friendly agricultural producers and consumers. In light of this, this study divided consumers based on their characteristics (such as age, purchase history, health concerns, etc.), and different policies were proposed in order to increase purchasing factors for each group based on their characteristics. In order to gather information for this study, general citizens were asked about their perceptions of eco-friendly agricultural products, future purchase intentions and awareness, reliability, necessity, purpose, and information sources. A two-step clustering analysis was conducted using nominal and continuous variables simultaneously. The paper presents the three derived group differences (senior organic interest group, middle-aged organic interest group, and indifferent young organics) as well as group differences for the purchasing/non-purchasing factor analysis and policy improvement for each group. An eco-friendly agricultural product distribution proposal was made at the end of this article.

• 지능정보연구
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• 제25권1호
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• pp.139-161
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• 2019

전자상거래 시장의 이용이 보편화 되며 고객들에게 좋은 품질의 물건을 어디서, 얼마나 합리적으로 구매할 수 있는지가 중요해졌다. 이러한 구매 심리의 변화는 방대한 정보 속에서 오히려 고객들의 구매 의사결정을 어렵게 만드는 경향이 있다. 이때 추천 시스템은 고객의 구매 행동을 분석하여 정보 검색에 드는 비용을 줄이고 만족도를 높이는 효과가 있다. 하지만 대부분 추천 시스템은 책이나 영화 등 동종 상품 분류 내에서만 추천이 이뤄진다. 왜냐하면 추천 시스템은 특정 상품에 매긴 구매 평점 데이터를 기반으로 해당 상품 분류 내 유사한 상품에 대한 구매 만족도를 추정하기 때문이다. 그밖에 추천 시스템에서 사용하는 구매 평점의 신뢰성에 대한 문제도 제시되고 있으며 오프라인에선 평점 확보 자체가 어렵다. 이에 본 연구에서는 일련의 문제를 개선하기 위해 RFM 다차원 분석 기법을 활용하여 기존에 사용하던 고객의 구매 평점을 객관적으로 대체할 수 있는 새로운 지표의 활용 가능성을 제안하는 바이다. 실제 기업의 구매 이력 데이터에 해당 지표를 적용해서 검증해본 결과 높게는 약 55%에 해당하는 정확도를 기록했다. 이는 총 4,386종에 달하는 이종 상품들 중 한번도 이용해 본 적 없는 상품을 추천한 결과이기 때문에 검증 결과는 상대적으로 높은 정확도와 활용가치를 의미한다. 그리고 본 연구는 오프라인의 다양한 상품데이터에서도 적용할 수 있는 범용적인 추천 시스템의 가능성을 시사한다. 향후 추가적인 데이터를 확보한다면 제안하는 추천 시스템의 정확도 향상도 기대할 수 있다.

• 한국의사학회지
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• 제29권2호
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• pp.49-59
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• 2016

Ginseng, a Korean native herb, has been a symbol of mystic cure-all which provides longevity benefits throughout Korean history. According to Chinese historical records, a major source of wild ginseng has been described as Korean peninsula, which is the most suitable ginseng production area, and the Manchu region near Mt. Baekdu and the Maritime Province. Since Tang dynasty period (618-917 CE), Chinese has also called ginseng as "Goryeo Ginseng", which is named after "Goguryeo" (37 BCE-668 CE), an ancient kingdom of Korea, from which they mainly imported the herb as the region was famous for its high quality of ginseng. To date, it refers to Korean ginseng. This study compares the medicinal properties of ginseng as stated in the ancient Korean medical books with the major Korean historical records regarding the usage of ginseng and its symbolism of longevity in everyday life. By contrasting these findings, we tried to figure out how the actual medicinal properties of ginseng and the anticipation of longevity are related. It was confirmed that the expectations about longevity were widely applied to everyday life. In addition, the study investigates the various usage of ginseng as a motive for decorative patterns and as an ingredient for daily products including snacks, health drinks, various types of food, clothing patterns, and so on. Finally, the usage of ginseng ingredients in the cosmetic products fulfilled the desire of Korean people to purchase, showing the aesthetic recognition and medicinal understandings about the herb. These findings suggest that ginseng is an important medicinal agent that not only symbolizes longevity and good health but also has a great influence on the lives of Koreans.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.