• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.493-496
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• 2018

추천 시스템은 다양한 분야에 적용되는 기술로서 활발한 연구가 진행되고 있고 기존 추천 시스템의 성능을 높이기 위해서 더욱 개인화된 차세대 추천 시스템의 필요성이 대두되고 있다. 본 논문은 하이퍼 개인화 범주에 속하는 사후 필터링기법을 사용한 실시간 상황 인식 추천 시스템을 제안한다. 실시간 상황 인식 추천 시스템은 사용자 행동과 계속적인 동기화로 현재 상황에 가장 적합한 추천 목록을 생성하기 때문에 사용자 기반 협업 필터링 (User Based Collaborative Filtering), 콘텐츠 기반 필터링(Content-based Filtering), 특이값 분해(Singular Value Decomposition)보다 훨씬 미래 지향적인 추천 시스템이다.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.111-126
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• 2006

The context-awareness is one of the core technologies in ubiquitous computing environment. In this research, we incorporated the capability of context-awareness in a case-based music recommendation system. Our proposed system consists of Intention Module and Recommendation Module. The Intention Module infers whether a user wants to listen to the music or not from the environmental context information. Then, the Recommendation Module selects songs from the songs that are listened by similar users in similar context, and recommends them to the user. The results showed that our proposed system outperformed the traditional case-based music recommendation system in accuracy by about 9% point.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.81-89
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• 2007

In this thesis, we propose ontology based context-aware recommendation system using concept hierarchy(OCARCH), Context-aware recommendation services are useful to provide an user with relevant information and/or services bared on his current context, However several approaches to context-aware recommendation system have been already proposed, each of them provide information without considering level of information concept bared on his current context, For this reason, we propose OCARCH as system capable of helping people to find their way quickly and easily through large amounts of information by determining level of information concept based on his current context, We are also using prefetching algorithm to store recommendation information that the user is likely to need in the near future based on current predictions, Therefore the OCARCH enables users to obtain relevant information efficiently, Several experiments are performed and the experimental results show that the proposed system provides more effective than conventional context-aware recommendation system.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.112-120
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• 2011

This research aims to show that the context adaptation system can be used to develop practical context-aware applications by developing a restaurant recommendation system based on location-awareness. In this research, we have designed and implemented a location-aware restaurant recommendation system which provides a customized restaurant recommendation service based on the user's current context. The context-adaptation engine adapts the application program according to the policy file as contexts are changed, and the application provides restaurant recommendation service based on the changed context like location.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.15-28
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• 2015

As personal devices and pervasive technologies for interacting with networked objects continue to proliferate, there is an unprecedented world of scattered pieces of contextualized information available. However, the explosive growth and variety of information ironically lead users and service providers to make poor decision. In this situation, recommender systems may be a valuable alternative for dealing with these information overload. But they failed to utilize various types of contextual information. In this study, we suggest a methodology for context-aware recommender systems based on the concept of contextual boundary. First, as we suggest contextual boundary-based profiling which reflects contextual data with proper interpretation and structure, we attempt to solve complexity problem in context-aware recommender systems. Second, in neighbor formation with contextual information, our methodology can be expected to solve sparsity and cold-start problem in traditional recommender systems. Finally, we suggest a methodology about context support score-based recommendation generation. Consequently, our methodology can be first step for expanding application of researches on recommender systems. Moreover, as we suggest a flexible model with consideration of new technological development, it will show high performance regardless of their domains. Therefore, we expect that marketers or service providers can easily adopt according to their technical support.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.299-304
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• 2006

기존에 개발된 사용자 선호 정보를 이용한 TV 추천 시스템은 대부분 사용자의 명시적인 요구에 따라 방송 프로그램을 추천하는 데 중점을 두고 개발되었다. 하지만, 유비쿼터스 환경이 도래함에 따라서 사용자의 요구에 따라 반응하는 수동적인 서비스 보다는 상황정보(Context)를 활용하여 능동적인 서비스를 지원할 수 있는 기술이 필요하다. 따라서 본 논문에서는 CAMUS(Context-Aware Middleware for URC Systems) 상황인식 미들웨어를 이용하여 사용자 위치 상황정보에 따라 능동적으로 추천할 수 있는 TV 추천 서비스 에이전트를 제안한다. 제안된 시스템은 CAMUS 기반 서비스 에이전트와 태스크를 구현함으로써, 상황정보에 따라 능동적으로 다채널에서 방송되는 프로그램 및 사용자의 선호도 정보를 분석하여 사용자가 원하는 프로그램을 추천한다.

• The Journal of the Korea Contents Association
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• v.8 no.7
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• pp.17-25
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• 2008

The smart home environment focuses on recognizing the context and physical entities. And this is mainly focused on the personalized service supplied conversational interactions. In this paper, we proposed the recommendation using the context awareness based information filtering that dynamically applied by the context awareness as well as the meta data in the smart home. The proposed method defined the context information and recommended the profited service for the user’s taste using the context awareness based information filtering. Accordingly, the satisfaction of users and the quality of services will be improved the efficient recommendation by supporting the distributed processing as well as the mobility of services. Finally, to evaluate the performance of the proposed method, this study applies to MovieLens dataset in the OSGi framework, and it is compared with the performance of previous studies.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.375-381
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• 2019

Recently, the recommendation system has attracted the attention of users as customized recommendation services have been provided focusing on fashion, video and music. But these services are difficult to provide users with proper service according to many different contexts because they do not use contextual information emerging in real time. When applied contextual information expands dimensions, it also increases data sparsity and makes it impossible to recommend proper music for users. Trying to solve these problems, our study proposed a music recommendation system to recommend proper music in real time by applying association rules and using relationships and rules about the current location and time information of users. The accuracy of the recommendation system was measured according to location and time information through 5-fold cross validation. As a result, it was found that the accuracy of the recommendation system was improved as contextual information accumulated.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.22-30
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• 2011

In the IT convergence environment changed with not only the quality but also the material abundance, it is the most crucial factor for the strategy of personalized recommendation services to investigate the context information. In this paper, we proposed the recommendation using the service ontology based context awareness modeling. The proposed method establishes a data acquisition model based on the OSGi framework and develops a context information model based on ontology in order to perform the device environment between different kinds of systems. In addition, the context information will be extracted and classified for implementing the recommendation system used for the context information model. This study develops the ontology based context awareness model using the context information and applies it to the recommendation of the collaborative filtering. The context awareness model reflects the information that selects services according to the context using the Naive Bayes classifier and provides it to users. To evaluate the performance of the proposed method, we conducted sample T-tests so as to verify usefulness. This evaluation found that the difference of satisfaction by service was statistically meaningful, and showed high satisfaction.

• 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.