• Journal of KIISE
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• v.43 no.10
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• pp.1144-1153
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• 2016

Group recommendation analyzes the characteristics and tendency of a group rather than an individual and provides relevant information for the members of the group. Existing group recommendation methods merely consider the average and frequency of a preference. However, if the users' preferences have large deviations, it is difficult to provide satisfactory results for all users in the group, although the average and frequency values are high. To solve these problems, we propose a method that considers not only the average of a preference but also the deviation. The proposed method provides recommendations with high average values and low deviations for the preference, so it reflects the tendency of all group members better than existing group recommendation methods. Through a comparative experiment, we prove that the proposed method has better performance than existing methods, and verify that it has high performance in groups with a large number of members as well as in small groups.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.11-20
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• 2013

Recently, as SNS services have been increased, studies on recommendation schemes have been actively done. Recommendation scheme provides various favorable or needed services with users on real time. Group recommendation provides users with suitable groups based on their preference. In this paper, we propose a new group recommendation scheme considering user profiles and collaborative filtering in social networks. The proposed scheme can solve the problems of the static profile based group recommendation scheme because it collects the recent group activities and updates user profiles. It also recommends the more various groups by reflecting the similar tendencies of other users within a group through collaborative filtering. Our experimental results show that the proposed scheme recommends various groups that significantly considers the user's changing preferences compared to the existing scheme.

• Journal of KIISE
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• v.43 no.7
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• pp.786-794
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• 2016

Recommender systems can be used to assist users in selecting required services for their tasks in Internet of Things (IoT) environments in which diverse services can be provided by utilizing IoT devices. Traditional research on recommendation mainly focuses on predicting preferences of individual users. However, in IoT environments, not only individual users but also groups of users can access services in the environments. In this study, we analyzed user groups' preferences on services and developed service recommendation approach for new groups that do not have a history of accessing IoT-services in a certain place. Our approach extends the traditional user-based collaborative filtering by considering the similarity between user groups based on their member organization. We conducted experiments with a real-world dataset collected from IoT testbed environments. The results demonstrate that the proposed approach is effective to recommend services to new user groups in IoT environments.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.417-418
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• 2008

본 연구에서는 웹 서비스의 종류가 급격히 증가하게 됨에 따라 유사 패턴의 사용자들을 위해 웹 링크 서비스를 일부 추천해주는 시스템에 대해 설계 및 구현하였다. 본 연구를 통해 유사 패턴의 웹 서비스 이용자들의 그룹을 정의 하는데 네이브 베이지안 알고리즘을 적응하고 그에 따른 새로운 사용자에 대한 그룹정의도 함께 한다. 유사 패턴의 그룹의 사용자들에게 적합한 링크들을 추천해준다. 기존의 추천 시스템에서 제공하는 추천 아이템을 제정의 하는 것이 아니라 기존의 웹 서비스 페이지에서 유사 패턴의 그룹에게만 일부의 링크들만 활성화 하여 제공한다. 이는 웹 서비스의 일부 링크 서비스들만을 활성화 하여 추천 해줌으로써 웹 서비스의 모바일 디바이스등에 제공시 웹 페이지의 소스를 경감하여 좀 더 수월하게 서비스 할 수 있다. 또한 사용자들도 추천 받은 링크만을 접근하게 됨에 따라 접근하지 않는 다른 서비스에 대한 링크 소스가 빠진 웹 페이지만 제공 받을 수 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.117-120
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• 2012

본 논문에서는 토픽 모델링 기반 TV 프로그램 유사 시청 사용자 그룹핑 및 이를 이용한 TV 프로그램 콘텐츠 추천 알고리듬을 제안하였다. 제안 기술은 토픽 모델링 기법 중 Latent Dirichlet Allocation(LDA) 방법을 이용하여 TV프로그램 시청 기록 내에서 은닉된 유사 사용자들을 그룹핑하고 이러한 유사 시청 사용자 그룹 정보를 이용하여 사용자에게 선호 TV 프로그램 콘텐츠를 자동으로 추천하는 알고리듬이다. 제안된 자동 추천 알고리듬의 성능평가를 위해 실제 TV 시청기록 데이터를 이용하여 훈련 기간과 검증 기간을 나누어 훈련 기간 동안 제안한 알고리듬을 이용하여 사용자 개인에 대한 추천 TV 프로그램 콘텐츠 목록을 생성하여 검증 기간 동안에 실제 추천된 TV프로그램을 얼마나 시청했는지를 측정하여 추천 정확도를 검증하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.55-58
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• 2012

본 논문은 사용자의 각종 멀티미디어 콘텐츠 소비 히스토리를 수집하여 체계화 및 패턴 분석을 수행하고, 이를 바탕으로 사용자가 선호할 것으로 예측되는 멀티미디어 콘텐츠들을 추출하여 제공하는 콘텐츠 추천 시스템에 관한 연구이다. 본 논문에서는 콘텐츠 소비와 연관된 사용자 로그와 엔진에서 자동 추출한 사용자 그룹을 통하여 콘텐츠 추천을 수행한다. 각 사용자들의 선호정보 데이터를 분석하여 선호정보 패턴이 유사한 사용자들을 사용자 그룹으로 정의하고, 각 사용자들이 속한 사용자 그룹의 사용자 로그를 활용하여 사용자별 선호 콘텐츠를 예측한다. 본 시스템은 웹 또는 모바일 환경에서 음악, 방송, 광고, 기사 등의 방대하고 다양한 콘텐츠를 복합적으로 사용자들에게 선별하여 제공해 주며, 이들의 연관성과 사용자의 콘텐츠 선호패턴을 반영한 개인 맞춤형 콘텐츠 추천 엔진은 사용자가 선호할만한 콘텐츠들을 추천하여 사용자의 콘텐츠 소비 시의 만족도를 높여줄 수 있다.

• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.421-428
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• 2008

Collaborative filtering is a popular technique used for the recommendation system, but its performance, especially the accuracy of recommendation, depends on how to define the reference group. This paper proposes a new contents recommendation scheme based on collaborative filtering technique whose reference groups are created by consumer's affection and consumption type in order to improve the accuracy of recommendation. In this paper, joy, sadness, anger, happiness, and relax are considered as the consumer's affection. And, low-utility / low-pleasure, low-utility / high-pleasure, high-utility / low-pleasure, and high-utility / high-pleasure are considered as the consumer's shopping types. Experimental results show that the proposed scheme improves the accuracy of recommendation compared to the recommendation scheme considering neither consumer's affection nor consumption type.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.298-305
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• 2009

In contrast to content_based filtering systems, collaborative filtering systems not only don't contain information of items, they can not recommend items when users don't provide the information of their interests. In this paper, we propose the recommender agent using association item tree to solve the shortcomings of collaborative filtering systems. Firstly, the proposed method clusters users into groups using vector space model and K-means algorithm and selects group typical rating values. Secondly, the degree of associations between items is extracted from computing mutual information between items and an associative item tree is generated by group. Finally, the method recommends items to an active user by using a group typical rating value and an association item tree. The recommender agent recommends items by combining user information with item information. In addition, it can accurately recommend items to an active user, whose information is insufficient at first rate, by using an association item tree based on mutual information for the similarity between items. The proposed method is compared with previous methods on the data set of MovieLens recommender system.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.7-11
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• 2013

Taking experts' knowledge to recommend items has shown some promising results in recommender system research. In order to improve the performance of the existing recommendation algorithms, previous researches on expert-based recommender systems have exploited the knowledge of a common expert group for all users. In this paper, we study a problem of identifying personalized experts within a user group, assuming each user needs different kinds and levels of expert help. To demonstrate this idea, we present a framework for using Support Vector Machine (SVM) to find varying expert groups for users; it is shown in an experiment that the proposed SVM approach can identify personalized experts, and that the person-alized expert-based collaborative filtering (CF) can yield better results than k-Nearest Neighbor (kNN) algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.768-774
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• 2016

This paper deals with a method to recommend the combination of items as a group according to similarity to handle application area such as fashion and cooking, while the previous methods recommend single item such as a book, music or movie. Collaborative filtering is a method to recommend an item selected by users with similar tendency based on similarity between users. In this paper, the proposed method generates a set of frequent items based on collaborative filtering and association rules and recommends a group by similarity between groups. To show the validity of the proposed method, experiments are performed with purchase data collected from e-commerce for four months.