• Asia pacific journal of information systems
• /
• 제14권2호
• /
• pp.131-149
• /
• 2004

Collaborative filtering is one of popular techniques for personalized recommendation in e-commerce sites. An advantage of collaborative filtering is that the technique can work with sparse evaluation data to predict preference scores of new alternative contents or advertisements. There is, however, no in-depth study about the sparsity effect of customer's evaluation data to the performance of recommendation. In this study, we investigate the sparsity effect and hybrid usages of customers' evaluation data and purchase data using an experiment result. The result of the analysis shows that the performance of recommendation decreases monotonically as the sparsity increases, and also the hybrid usage of two different types of data; customers' evaluation data and purchase data helps to increase the performance of recommendation in sparsity situation.

• 한국경영과학회지
• /
• 제10권1호
• /
• pp.14-30
• /
• 1985

Recently, multiple criteria decision making has been well established as a practical approach to seek a satisfactory solution to a decision making problem. Goal programming is one of the most powerful MCDM tools with satisfying operational assumptions that reflect the actual decision making process in real-world situations. In this paper we propose an efficient method implemented on a microcomputer for solving linear goal programming problems. It is an iterative revised goal simplex method using the sparsity technique. We design as interactive software package for microcomputers based on this method. From some computational experiences, we can state that the revised iterative goal simplex method using the sparsity technique is the most efficient one for microcomputer for solving goal programming problems.

• 대한전기학회논문지
• /
• 제45권3호
• /
• pp.329-334
• /
• 1996

Power system is becoming more and more complex and large Existing procedural programming technique can't cope with software flexibility and maintenance problems. So, Object-Oriented Programming (OOP) is increasingly used to solve these problems. OOP in power system analysis field has been greatly developed. This paper applies OOP in power flow analysis, and presents new algorithm which uses only a Jacobian to solve mismatch equations, and introduces a new sparse matrix storage method which is different from existing method. (author). 11 refs., 12 figs., 3 tabs.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권8호
• /
• pp.3498-3511
• /
• 2016

We investigate the channel state information (CSI) in multi-input multi-output (MIMO) cooperative networks that employ the amplify-and-forward transmission scheme. Least squares and expectation conditional maximization have been proposed in the system. However, neither of these two approaches takes advantage of channel sparsity, and they cause estimation performance loss. Unlike linear channel estimation methods, several compressed channel estimation methods are proposed in this study to exploit the sparsity of the MIMO cooperative channels based on the theory of compressed sensing. First, the channel estimation problem is formulated as a compressed sensing problem by using sparse decomposition theory. Second, the lower bound is derived for the estimation, and the MIMO relay channel is reconstructed via compressive sampling matching pursuit algorithms. Finally, based on this model, we propose a novel algorithm so called sparsity adaptive expectation maximization (SAEM) by using Kalman filter and expectation maximization algorithm so that it can exploit channel sparsity alternatively and also track the true support set of time-varying channel. Kalman filter is used to provide soft information of transmitted signals to the EM-based algorithm. Various numerical simulation results indicate that the proposed sparse channel estimation technique outperforms the previous estimation schemes.

• ETRI Journal
• /
• 제39권1호
• /
• pp.76-86
• /
• 2017

In this study, we propose an automatic contrast enhancement method based on transfer function modification (TFM) by histogram equalization. Previous histogram-based global contrast enhancement techniques employ histogram modification, whereas we propose a direct TFM technique that considers the mean brightness of an image during contrast enhancement. The mean point shifting method using a transfer function is proposed to preserve the mean brightness of an image. In addition, the linearization of transfer function technique, which has a histogram flattening effect, is designed to reduce visual artifacts. An attenuation factor is automatically determined using the maximum value of the probability density function in an image to control its rate of contrast. A new quantitative measurement method called sparsity of a histogram is proposed to obtain a better objective comparison relative to previous global contrast enhancement methods. According to our experimental results, we demonstrated the performance of our proposed method based on generalized measures and the newly proposed measurement.

• Journal of Information Technology Applications and Management
• /
• 제16권1호
• /
• pp.97-113
• /
• 2009

Collaborative filtering(CF) has been a very successful approach for building recommender system, but its widespread use has exposed to some well-known problems including sparsity and scalability problems. In order to mitigate these problems, we propose two novel models for improving the typical CF algorithm, whose names are ISCF(Item-Selected CF) and USCF(User-Selected CF). The modified models of the conventional CF method that condense the original dataset by reducing a dimension of items or users in the user-item matrix may improve the prediction accuracy as well as the efficiency of the conventional CF algorithm. As a tool to optimize the reduction of a user-item matrix, our study proposes genetic algorithms. We believe that our approach may relieve the sparsity and scalability problems. To validate the applicability of ISCF and USCF, we applied them to the MovieLens dataset. Experimental results showed that both the efficiency and the accuracy were enhanced in our proposed models.

• 한국정보통신학회논문지
• /
• 제15권4호
• /
• pp.877-885
• /
• 2011

협업 필터링은 추천시스템들 중에서 가장 널리 사용되는 기법이다. 그러나 협업 필터링은 추천의 정확성을 떨어뜨리는 희소성과 확장성 문제를 가지고 있으며 이를 해결하기 위한 다양한 연구가 이루어지고 있다. 본 논문에서는 협업필터링의 희소성과 확장성의 문제를 해결하기 위해 가중치를 사용한 기법을 제안한다. 제안한 기법은 데이터 셋에서 추천의 정확성을 높이기 위해 평가값이 4이상인 데이터들만을 사용하여 아이템을 선호하는 사용자 정보를 분석한다. 아이템의 장르 정보와 분석한 사용자 정보를 유사도 계산 시 가중치로 사용하고 임계값 이상의 유사도를 가진 데이터들만으로 예측값을 계산하여 평가되지 않은 데이터의 평가값으로 사용한다. 제안한 기법은 아이템에 대한 특성을 분석하여 예측값을 계산함으로써 희소성을 줄임과 동시에 정확성을 더 높일 수 있고 새로운 아이템과 사용자가 등록되었을 때 분석된 정보를 바탕으로 빠른 분류가 가능하다. 실험을 통해 제안한 기법이 기존의 아이템 기반, 장르 기반 기법보다 추천의 정확성이 향상되는 것을 확인하였다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
• /
• 제2권11호
• /
• pp.475-482
• /
• 2013

추천 시스템들에 대한 여러 방법들이 연구되고 있다. 개인화와 추천 시스템 중에서 가장 성공적인 방법은 협업 필터링이다. 협업 필터링은 고객들의 프로파일 정보를 기반으로 추천을 하므로 데이터가 충분하지 않다면 항목을 추천하는데 있어서 희박성의 문제가 제기된다. 본 연구에서는 희박성의 문제를 해결하는 방법으로 가중치를 가진 개인 성향을 협업 필터링에 활용하는 방법을 제안한다. 본 연구에서 가중치를 가진 최적의 개인 성향을 찾기 위해 공개 데이터인 MovieLens Data를 이용하여 성능 평가하였다. 실험 결과 본 연구에서 제안한 가중치를 가진 개인 성향들로 구축된 시스템이 기존의 개인 성향들을 이용한 시스템보다 향상된 성능을 보였다.

• 한국멀티미디어학회논문지
• /
• 제19권2호
• /
• pp.200-208
• /
• 2016

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in an under-sampled (i.e., under Nyquist rate) representation. A block compressed sensing with projected Landweber (BCS-SPL) framework is most widely known, but, it has high computational complexity at decoder side. In this paper, by introducing adaptive exit criteria instead of fixed exit criteria to SPL framework, an effective fast algorithm is designed in such a way that it can utilize efficiently the sparsity property in DCT coefficients during the iterative thresholding process. Experimental results show that the proposed algorithm results in the significant reduction of the decoding time, while providing better visual qualities than conventional algorithm.

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