• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.246-254
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• 2007

최근 유비쿼터스 환경이 대두됨에 따라 정보의 밀도가 높아지고 있으며, 기업에서는 고객이 제품을 구매함과 동시에 고객의 정보를 저장하여 활용할 수 있게 되었다. 이와 같은 환경은 고객의 요구사항을 사전에 미리 파악하여 적절한 시점과 상황에 맞는 정보를 전달할 수 있도록 하는 추천시스템에 대한 필요성을 증대시켰으며, 다양한 영역에서 추천시스템과 관련된 연구들이 활발하게 이루어지고 있다. 지금까지의 추천시스템은 주로 제품 중심으로 논의되어 왔으나, 유비쿼터스 시장 환경에서는 매장에 대한 논의가 필요하게 되었다. 이는 고객이 다양한 매장을 방문할 수 있으며, 동일한 제품이라도 여러 매장에 동시에 존재할 수 있고, 매장 간의 동선이나 매장의 위치 및 분위기, 제품의 품질이나 가격 등에 대한 개인 선호도에 따라 같은 제품이라도 선호하는 매장은 다를 수 있기 때문이다. 따라서 본 연구에서는 고객의 선호도를 기반으로 유비쿼터스 시장 환경에 적합한 매장 추천시스템을 제안하고자 한다. 매장 추천시스템은 협업 필터링을 기반으로 하고 있으며, Apriori 알고리즘을 이용하여 관련성이 높은 매장들의 집합을 찾아 추천한다. 이 시스템은 기업보다는 고객 중심의 서비스를 제공해 줌으로써 고객의 쇼핑 효율성을 제고시킬 뿐 아니라 장기적인 관점에서 시장 활성화에 기여할 수 있을 것으로 기대한다.

• Information Systems Review
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• v.19 no.2
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• pp.71-94
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• 2017

Recommender systems reduce information overload and enhance choice quality. This technology is used in many services and industry. Previous studies did not consider recommendation quantity and the repetitive recommendations of an item. This study is the first to examine recommender systems by considering recommendation quantity and repetitive recommendations. Only a limited number of items are displayed in offline stores because of their physical limitations. Determining the type and number of items that will be displayed is an important consideration. In this study, I suggest the use of a user-based recommender system that can recommend the most appropriate items for each store. This model is evaluated by MAE, Precision, Recall, and F1 measure, and shows higher performance than the baseline model. I also suggest a new performance evaluation measure that includes Quantity Precision, Quantity Recall, and Quantity F1 measure. This measure considers the penalty for short or excess recommendation quantity. Novelty is defined as the proportion of items in a recommendation list that consumers may not experience. I evaluate the new revenue creation effect of the suggested model using this novelty measure. Previous research focused on recommendations for customer online, but I expand the recommender system to cover stores offline.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.45-63
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• 2007

Recently as ubiquitous environment comes to the fore, information density is raised and enterprise is being able to capture and utilize customer-related information at the same time when the customer purchases a product. In this environment, a need for the recommender systems which can deliver proper information to the customer at the right time and right situation is highly increased. Therefore, the research on recommender systems continued actively in a variety of fields. Until now, most of recommender systems deal with item recommendation. However, in the market in ubiquitous environment where the same item can be purchased at several stores, it is highly desirable to recommend store to the customer based on his/her contextual situation and preference such as store location, store atmosphere, product quality and price, etc. In this line of research, we proposed the store recommender system using customer's contextual situation and preference in the market in ubiquitous environment. This system is based on collaborative filtering and Apriori algorithms. It will be able to provide customer-centric service to the customer, enhance shopping experiences and contribute in revitalizing market in the long term.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.443-446
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• 2014

최근 인터넷을 이용한 의류 관련 쇼핑몰이 증가되고 있다. 그에 따라 이용자들도 오프라인 매장뿐만 아니라 온라인 쇼핑몰 매장을 즐겨 찾고 있으며, 이에 따라 온라인 쇼핑몰의 차별성이 중요시 되고 있다. 본 논문에서는 이러한 온라인 쇼핑몰의 차별성을 극대화하기 위해서 오프라인 매장의 장점을 살리고자 하였다. 오프라인 매장에서는 매장 직원에 의한 상의 하의 추천이 가능하고 이에 따라 매출향상 및 이용자의 만족도를 이끌어내고 있다. 이러한 오프라인 매장의 장점을 온라인에서도 그대로 실현하기 위해서 의류의 색상을 계산하여 나오는 색상 값을 색조합표를 기반으로 하여 가장 어울리는 의류에 대해 자동으로 추천해주는 시스템을 설계하였다.

• Information Systems Review
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• v.10 no.2
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• pp.195-209
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• 2008

Customers' needs change every moment. Profitability of stores can't be increased anymore with an existing standardized chain store management. Accordingly, a personalized store management tool needs through prediction of customers' preference. In this study, we propose a recommending procedure using dynamic customers' preference by analyzing the transaction database. We utilize self-organizing map algorithm and association rule mining which are applied to cluster the chain stores and explore purchase sequence of customers. We demonstrate that the proposed methodology makes an effect on recommendation of products in the market which is characterized by a fast fashion and a short product life cycle.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.359-360
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• 2013

본 논문에서는 기존의 쿠폰의 사용자 편의성을 극대화하기 위하여, NFC를 기반으로 하는 모바일 쿠폰 추천 시스템을 제안한다. 기존의 쿠폰은 사용자에게 무작위로 정보를 제공하고, 종이 쿠폰의 경우 분실의 가성능과 여러 장을 소지해야 하는 단점이 있었다. 따라서 본 논문에서는 NFC기반으로 사용자에게 마일리지를 판매자의 홍보 수단으로 제공하는 한편 이를 이용해 얻어지는 판매자들의 매장 이용 현황의 유사도를 분석하여, 비슷한 유사도를 보이는 사용자를 기반으로 판매자의 매장의 쿠폰을 추천하는 쿠폰 추천 시스템을 구현하였다

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.197-200
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• 2007

상품 판매장에서 많은 상품을 판매하기 위해서는 매장 내에서 구매자 행동과 상품 배치 등 매상에 영향을 미치는 다양한 요인을 파악할 필요가 있다. 또한 모바일 커머스 어플리케이션에서 각 구매자들이 구입할 상품을 효과적으로 찾을 수 있는 추천상품 서비스의 필요성도 점차 증가하고 있다. 본 논문에서는 다치 오토마타를 이용하여 매장 내에서 구매자 행동과 상품 배치 등을 파악함과 동시에 각 구매자들이 구입할 상품을 상황의 변화에 따라 효과적으로 추천할 수 있도록 지원하는 상황인식 기반 모바일 구매 서비스 모델을 제안한다.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.871-880
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• 2018

Recently, the technology of recommendation of POI (Point of Interest) related technology is getting attention with the increase of big data related to consumers. Previous studies on POI recommendation systems have been limited to specific data sets. The problem is that if the study is carried out with this particular dataset, it may be suitable for the particular dataset. Therefore, this study analyzes the similarity and correlation between stores using the user visit data obtained from the integrated sensor installed in Seoul and Songjeong roads. Based on the results of the analysis, we study the preference prediction system which recommends the stores that new users are interested in. As a result of the experiment, various similarity and correlation analysis were carried out to obtain a list of relevant stores and a list of stores with low relevance. In addition, we performed a comparative experiment on the preference prediction accuracy under various conditions. As a result, it was confirmed that the jacquard similarity based item collaboration filtering method has higher accuracy than other methods.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.437-441
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• 2006

본 논문에서는 RFID 위치추적엔진과 지능형 에이전트를 이용한 선호상품 추천 기법을 이용하여 RFID기반 위치추적을 이용한 실시간 선호 상품 추천 시스템을 제안한다. 매장안에서 RFID 태그가 부착된 스마트 카트를 이용하여 고객의 위치를 실시간으로 파악하여 각 구역별 쇼핑시간과 개별 고객의 구매 히스토리 분석 및 이동 구역 예측을 통해 실시간으로 쇼핑 매장에서 각 고객의 선호상품을 추천한다.