• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.714-717
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• 2008

협업여과 추천기법에는 사용자 기반 협업여과와 아이템 기반 협업여과가 있으며, 절차는 유사도 측정, 이웃 선정, 예측값 생성 단계로 이루어진다. 유사도 측정 단계에는 유클리드 거리(Euclidean Distance), 코사인 유사도(Cosine Similarity), 피어슨 상관계수(Pearson Correlation Coefficient) 방법 등이 있고, 이웃 선정 단계에는 상관 한계치(Correlation-Threshold), 근접 N 이웃(Best-N-Neighbors) 방법 등이 있다. 마지막으로 예측값 생성 단계에는 단순평균(Simple Average), 가중합(Weighted Sum), 조정 가중합(Adjusted Weighted Sum) 등이 있다. 이처럼 협업여과 추천기법에는 다양한 기법들이 사용되고 있다. 따라서 본 논문에서는 사용자 기반 협업여과와 아이템 기반 협업여과 추천기법에 사용되는 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 알아보기 위해 성능 실험 및 비교 분석을 하였다. 실험은 GroupLens의 MovieLens 데이터 셋을 활용하였고 MAE(Mean Absolute Error)값을 이용하여 추천기법을 비교 하였다. 실험을 통해 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 찾을 수 있었고, 사용자 기반 협업여과와 아이템 기반 협업여과의 성능비교를 통해 아이템 기반 협업여과의 성능이 보다 우수했음을 확인 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1081-1084
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• 2012

최근 다양한 직업을 가진 SNS 사용자가 증가함에 따라, SNS 사용자들은 전문가 간 협업 및 기술적 의사소통을 위한 전문가 추천 기능을 요구하고 있다. 하지만 기존 협업적 여과 기법은 전문가 추천 서비스를 효율적으로 제공하지 못한다. Content-boosted 협업적 여과 기법은 다양한 예측 알고리즘을 제시하여, 효과적인 추천을 수행할 수 있도록 지원한다. 그러나 명확한 계산 조건이 제시되지 못하는 경우 아이템 및 사용자 유사도 계산을 수행할 수 없는 단점이 존재한다. 따라서 본 논문에서는 Content-boosted 협업적 여과 기법의 단점을 해결하는 하이브리드 협업적 여과기법을 이용한 새로운 전문가 추천기법을 제안한다. 또한, 이를 이용하여 SNS에서의 전문가 추천 시스템을 설계한다.

• 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.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.55-62
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• 2009

Collaborative filtering is a popular technique that recommends items based on the opinions of other people in recommender systems. Memory-based collaborative filtering which uses user database can be divided in user-based approaches and item-based approaches. User-based collaborative filtering predicts a user's preference of an item using the preferences of similar neighborhood, while item-based collaborative filtering predicts the preference of an item based on the similarity of items. This paper proposes a combined forecast scheme that predicts the preference of a user to an item by combining user-based prediction and item-based prediction using the ratio of the number of similar users and the number of similar items. Experimental results using MovieLens data set and the BookCrossing data set show that the proposed scheme improves the accuracy of prediction for movies and books compared with the user-based scheme and item-based scheme.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.81-98
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• 2009

Collaborative filtering, one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information and supporting the decision making. However, despite of its success and popularity, one notable issue is incredibility of recommendations by unreliable users called shilling attacks. To deal with this problem, in this paper, we analyze the type of shilling attacks and propose a unique method of building a model for protecting the recommender system against manipulated ratings. In addition, we present a method of applying the model to collaborative filtering which is highly robust and stable to shilling attacks.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.5
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• pp.521-529
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• 2003

In this paper we present an improved method by using demographic information for overcoming the similarity miss-calculation from the sparsity problem in collaborative filtering recommendation systems. The similarity between a pair of users is only determined by the ratings given to co-rated items, so items that have not been rated by both users are ignored. To solve this problem, we add virtual neighbor's rating using demographic information of neighbors for improving prediction accuracy. It is one kind of extentions of traditional collaborative filtering methods using the peason correlation coefficient. We used the Grouplens movie rating data in experiment and we have compared the proposed method with the collaborative filtering methods by the mean absolute error and receive operating characteristic values. The results show that the proposed method is more efficient than the collaborative filtering methods using the pearson correlation coefficient about 9% in MAE and 13% in sensitivity of ROC.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.147-156
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• 2003

Collaborative Filtering is a popular technology in electronic commerce, which adapt the opinions of entire communities to provide interesting products or personalized resources and items. It has been applied to many kinds of electronic commerce domain since Collaborative Filtering has proven an accurate and reliable tool. But educational application remain limited yet. We design collaborative filtering recommendation system using user's ratings in educational contents recommendation. Also We propose a method of similarity compensation using user's information for improvement of recommendation accuracy. The proposed method is more efficient than the traditional collaborative filtering method by experimental comparisons of mean absolute error(MAE) and reciever operating characteristics(ROC) values.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.451-462
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• 2009

Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information. However, despite its success and popularity, CF encounters a serious limitation with quality evaluation, called cold start problems. To alleviate this limitation, in this paper, we propose a unique method of building models derived from explicit ratings and applying the models to CF recommender systems. The proposed method is divided into two phases, an offline phase and an online phase. First, the offline phase is a building pre-computed model phase in which most of tasks can be conducted. Second, the online phase is either a prediction or recommendation phase in which the models are used. In a model building phase, we first determine a priori predicted rating and subsequently identify prediction errors for each user. From this error information, an error-reflected model is constructed. The error-reflected model, which is reflected average prior prediction errors of user neighbors and item neighbors, can make accurate predictions in the situation where users or items have few opinions; this is known as the cold start problems. In addition, in order to reduce the re-building tasks, the error-reflected model is designed such that the model is updated effectively and users'new opinions are reflected incrementally, even when users present a new rating feedback.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.475-486
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• 2010

Contents Recommender System predicts user's preferences towards contents, and then recommends highly-predicted contents to user. Digital Identifier plays its part in identifying abstract works or digital contents in digital network environment. Digital Identifier could be effectively used in content-based filtering and collaborative filtering that are mainly used in Contents Recommender Systems. Therefore, this paper proposes an improvement of UCI metadata and resolution service for effective use of UCI in massive contents recommender systems. UCI metadata is expanded by adding elements such as abstract, keyword, genre, age, rate and review. Resolution service allows the operation systems to collect user preference for content by including input part of preference in a result page. This paper also designs and implements an improved UCI operation system and shows that the proposed improvement of UCI metadata and resolution service could be used for massive contents recommendation.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.185-193
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• 2010

With the emergence of ubiquitous computing era, various models for providing personalized service have been proposed, and, especially, several recommendation service schemes have been proposed to give tailored services to users proactively. However, the previous recommendation service schemes utilize a wide range of data without and filtering and consider the limited context-aware information to predict user preferences so that they are not adequate to provide personalized service to users. In this paper, we propose an adaptive recommendation service scheme which proactively provides suitable services based on the current context. We use accumulated interaction contexts (IC) between users and devices for predicting the user's preferences and recommend adaptive service based on the current context by utilizing clustering and collaborative filtering. The clustering algorithm improves efficiency of the recommendation service by focusing and analyzing the data that is collected from the locations nearby the users. Collaborative filtering guarantees an accurate recommendation, even when the data is insufficient. Finally, we evaluate the performance and the reliability of the proposed scheme by simulations.