• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.110-111
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• 2010

IPTV 서비스는 기존 단방향 지상파 TV보다 다양한 채널의 다양한 콘텐츠를 제공하므로 사용자가 빠르고 쉽게 원하는 콘텐츠를 찾기 위한 개인화 IPTV 사용자 인터페이스 개발이 요구되고 있다. 하지만 기존의 연구는 단방향 TV 서비스 인터페이스를 그대로 이용하거나 단순 카테고리 별로 분류된 정보 제공에 머물러 개인화된 인터페이스로는 아직 미흡하다. 본 논문에서는 협력적 여과, 내용기반 여과 등 기존 개인화 추천 기법을 이용하여 사용자가 원하는 정보를 빠르게 제공하고, 사용자의 편의성을 증대하는 SmarTVi 인터페이스와 개인화된 검색 결과를 제공하는 검색 모듈 SmarTVi-Search를 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.307-308
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• 2009

IPTV 서비스가 보급됨에 따라 다양한 콘텐츠 중에서 사용자가 원하는 콘텐츠를 빠르고 쉽게 찾기 위한 개인화 된 IPTV 사용자 인터페이스 개발이 요구되고 있다. 본 논문에서는 협력적 여과, 내용기반 여과 등 기존 개인화 추천 기법을 이용하여 사용자가 원하는 정보를 제공하고, 사용자의 편의성을 증대하는 SmarTV와 사용자가 원하는 검색 결과를 우선하여 제공하는 IPTV에 특화된 검색 모듈인 SmarTV-Search를 제안한다.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1111-1120
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• 2005

In collaborative filtering systems most users do not rate preferences; so User-Item matrix shows great sparsity because it has missing values for items not rated by users. Generally, the systems predict the preferences of an active user based on the preferences of a group of users. However, default voting methods predict all missing values for all users in User-Item matrix. One of the most common methods predicting default voting values tried two different approaches using the average rating for a user or using the average rating for an item. However, there is a problem that they did not consider the characteristics of items, users, and the distribution of data set. We replace the missing values in the User-Item matrix by the default noting method using user coefficient of variance. We select the threshold of user coefficient of variance by using equations automatically and determine when to shift between the user averages and item averages according to the threshold. However, there are not always regular relations between the averages and the thresholds of user coefficient of variances in datasets. It is caused that the distribution information of user coefficient of variances in datasets affects the threshold of user coefficient of variance as well as their average. We decide the threshold of user coefficient of valiance by combining them. We evaluate our method on MovieLens dataset of user ratings for movies and show that it outperforms previously default voting methods.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1054-1061
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• 2004

At recent, people can easily access to information by Internet to be rapidly evolving. And also, the amount is rapidly increasing. So the techniques, to automatically extract the required information are very important to reduce the time and the effort for retrieving information. In this paper, we describe a collaborative filtering system for automatically recommending high-quality information to users with similar interests on arbitrarily narrow information domains. It asks a user to rate a gauge set of items. It then evaluates the user's rates and suggests a recommendation set of items. We interpret the process of evaluation as an inference mechanism that maps a gauge set to a recommendation set. We accomplish the mapping with FAM (Fuzzy Associative Memory). We implemented the suggested system in a Web server and tested its performance in the domain of retrieval of technical papers, especially in the field of information technologies. The experimental results show that it may provide reliable recommendations.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.607-609
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• 2005

온라인 및 오프라인 상에서 추천시스템에 대한 요구가 커지고 있으며 이에 관련해 않은 연구가 이루어지고 있다. 추천시스템은 마케팅 활용의 관점에서 목표 상품에 대한 반응 가능성이 높은 고객군을 추천하는 타겟마케팅 추천시스템과 고객 개인별로 구매 가능성이 높은 상품을 추천하는 개인화 추천시스템으로 구분할 수 있다. 지금까지의 추천시스템에 관한 연구는 대부분 개인화 추천시스템의 효율 향상에 목표를 두고 있다. 그러나 기업의 타겟마케팅에 대한 요구를 적절히 지원하지 못하고 있어 타겟마케팅에 대한 연구가 필요하다. 본 연구에서는 상품별 구매 패턴을 이용하는 프로파일 기반 추천 방법을 제안하고 이 방법과 기존의 협력적 추천 방법을 결합하여 특정 상품에 반응 가능성이 높은 고객을 추천하는 방법을 제안한다. 프로파일 기반 추천에서는 카이스퀘어 검정을 사용하여 상품별로 구매 패턴에 영향을 미치는 요인을 추출하고 이를 이용하여 특징 고객군을 선별하여 전체 고객군과 특징 고객과의 엔트로피(Entropy)의 변이 정도를 예측값으로 사용한다. 실험결과, 프로파일 기반 추천과 협력적 추천을 결합하여 추천하는 방법은 한 가지 방법을 사용할 때 보다 좋은 추천 정확도를 나타내었다.

• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.248-257
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• 2002

Clustering is a method for grouping objects with similar properties into a same cluster. SVD(Singular Value Decomposition) is known as an efficient preprocessing method for clustering because of dimension reduction and noise elimination for a high dimensional and sparse data set like E-Commerce data set. However, it is hard to evaluate the worth of original attributes because of information loss of a converted data set by SVD. This research proposes a cluster feature selection method, called ENTROPY-SVD, to find important attributes for each cluster based on entropy weighting and SVD. Using SVD, one can take advantage of the latent structures in the association of attributes with similar objects and, using entropy weighting one can find highly dense attributes for each cluster. This paper also proposes a model-based collaborative filtering recommendation system with ENTROPY-SVD, called CFS-CF and evaluates its efficiency and utilization.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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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.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.581-591
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• 2005

Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.149-159
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• 2001

There have been lots of researches for providing a personalized service to a customer using one-to-one marketing and collaborative filtering techniques in E-Commerce. However, there are technical difficulties for providing the recommendation of products far users, which often involve high complexity of computation. In this paper, we have presented an integrated method of classification problem solving method and constraint based configuration techniques. This method can reduce a complexity of computation by classifying a solution domain space that has a higher complexity of composition. Thereafter, we have modeled customers constraints and the components of products to configure a complete system by passing it to constraint processing module in Constraint Satisfaction Problems. Constraint-based configuration uses the constraint propagation using the constraints of buyers and the constraints among PC components to configure a proper product for a customer. We have transformed and applied vector space modeling method in the field of information retrieval to consider a customer satisfaction in addition to the CSP. Finally, we have applied our system to test data fur evaluating a customers satisfaction and performance of the proposed system.