• 산업경영시스템학회지
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• 제27권1호
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• pp.38-46
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• 2004

To be successful in increasingly competitive Internet marketplace, it is essential to capture customer loyalty. This paper deals with an intelligent agent approach to incorporate customer's sensibility into an one-to-one recommendation service in on-line shopping mall. In this paper the focus of interest is on-line recommendation service algorithm for development of Human Sensibility based web agent system. The recommendation agent system composed of seven services including specialized algorithm. The on-line recommendation service algorithm use human sensibility ergonomics and on-line preference matching technologies to tailor to the customer the suggestion of goods and the description of store catalog. Customizing the system's behavior requires the parallel execution of several tasks during the interaction (e.g., identifying the customer's emotional preference and dynamically generating the pages of the store catalog). Most of the present shopping malls go through the catalog of goods, but the future shopping malls will have the form of intelligent shopping malls by applying the on-line recommendation service algorithm.

• 대한인간공학회지
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• 제23권3호
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• pp.1-12
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• 2004

In recent years, with the advent of e-Commerce the need for personalized services and one-to-one marketing has been emphasized. To be successful in increasingly competitive Internet marketplace, it is essential to capture customer loyalty. In this paper, we provide an intelligent agent approach to incorporate human sensibility into an one-to-one recommendation service in cyber shopping mall. Our system exploits human sensibility ergonomics and on-line preference matching technologies to tailor to the customer the suggestion of goods and the description of store catalog. Customizing the system`s behavior requires the parallel execution of several tasks during the interaction （e. g., identifying the customer`s emotional preference and dynamically generating the pages of the store catalog）. The recommendation agent system composed of five modules including specialized agents carries on these tasks. By presenting goods that are consistent with user interests as well as user sensibility, the accuracy and satisfaction of the recommendation service may be improved.

• 한국산학기술학회논문지
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• 제9권6호
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• pp.1625-1631
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• 2008

본 논문에서는 오프라인 쇼핑몰에서 RFID 실시간 위치추적 기술과 USB 메모리와 RF가 융합된 Tag를 이용하여 오프라인 쇼핑몰 고객의 쇼핑리스트를 관리하고 Tag를 통한 위치분석 데이터를 분석한 결과를 토대로 고객에게 실시간 대화형(Interactive) 서비스 제공을 위한 지능형 선호 상품 시스템을 제안한다.

• 한국멀티미디어학회논문지
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• 제7권4호
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• pp.592-600
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• 2004

전자 상거래 환경에서 e-CRM의 한 응용분야인 추천 시스템은 사용자 개개인의 요구를 충족하는 개인화된 품 추천 서비스를 제공한다. 일반적으로 기존 추천 시스템들은 응용 영역에 대한 방대한 과거 사용자 정보를 요로 한다. 그러나, 과거 정적인 사용자 정보 기반의 추천 방식은 다양한 사용자를 포함하는 영역 혹은 간에 민감하게 빠르게 변화하는 사용자 요구에 유연하게 대처하는 추천 방법이 필요하다. 또한, 해당영역의 존 사용자로부터 분류될 수 없는 새로운 사용자에 대한 추천을 어렵게 한다. 이러한 한계를 극복하고 유연한 추천 서비스를 위해 본 논문에서는 고객성향분석과 필터관리를 지원하는 CPAR (Customer Preference Analysis Recommender) 시스템을 설계하고 구현한다. 본 시스템의 필터 관리 능력은 기존 시스템의 방대한 초기 사용자 정보 필요 문제를 경감한다. 또한, CPAR 시스템은 플랫폼에 독립적이고 시간과 장소에 구애받지 않는 추천 서비스를 위해 XML 기반 무선 인터넷 환경에서 구현되었다.

• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.819-830
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• 2008

Web recommender system was suggested in order to solve the problem which is cause by overflow of information. Collaborative filtering is the technique which predicts and recommends the suitable goods to the user with collection of preference information based on the history which user was interested in. However, there is a difficulty of recommendation by lack of information of goods which have less popularity. In this paper, it has been researched the way to select the sparsity of goods and the preference in order to solve the problem of recommender system's sparsity which is occurred by lack of information, as well as it has been described the solution which develops the quality of recommender system by selection of customers who were interested in.

• 한국지능시스템학회논문지
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• 제13권3호
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.443-446
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• 2002

In this research, we suggest a Web-based hybrid recommendation mechanism using CBR (Case-Based Reasoning) and web data mining. Data mining is used as an efficient mechanism in reasoning for relationship between goods, customers' preference and future behavior. CBR systems are normally used in problems for which it is difficult to define rules. We use CBR as an AI tool to recommend the similar purchase case. A Web-log data gathered in real-world Internet shopping mall was given to illustrate the quality of the proposed mechanism. The results showed that the CBR and web data mining-based hybrid recommendation mechanism could reflect both association knowledge and purchase information about our former customers.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2008년도 추계학술발표논문집
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• pp.21-24
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• 2008

본 논문에서는 오프라인 쇼핑몰에서 위치추적 기술과 동선분석을 이용하여 오프라인 쇼핑몰 고객의 위치분석 데이터를 분석한 결과를 토대로 고객에게 실시간 대화형(Interactive) 서비스 제공을 위한 선호 상품 시스템을 설계하여 쇼핑효과를 극대화하며, 고객 만족도를 향상시킬 수 있도록 돕는데 그 목적이 있다.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2009년도 춘계학술발표논문집
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• pp.103-106
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• 2009

본 논문에서는 RFID 태그를 이용하여 고객의 위치를 인식할 수 있는 개체 인식 기술과 고객의 현재 위치 및 쇼핑 동선파악을 위한 데이터 무선 전송 및 저장 기술, 마지막으로 고객화된 정보를 자동으로 생성하고 적시에 해당 고객에게 제공해 줄 유비쿼터스형 에이전트 기술을 적용하여 쇼핑 선호 상품 추천 시스템을 설계 및 구현 하고자 한다.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2010년도 춘계학술발표논문집 1부
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• pp.539-542
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• 2010

본 논문에서는 오프라인 쇼핑몰에서 위치추적 기술과 동선분석을 이용하여 오프라인 쇼핑몰 고객의 위치분석 데이터를 분석한 결과를 토대로 고객에게 실시간 대화형(Interactive) 서비스 제공을 위한 선호 상품 시스템을 설계하여 쇼핑효과를 극대화하며, 고객 만족도를 향상시킬 수 있도록 돕는데 그 목적이 있다.