• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.77-82
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• 2019

Collaborative filtering is a popular technique for recommender systems and used in many practical commercial systems. Its basic principle is select similar neighbors of a current user and from their past preference information on items the system makes recommendations for the current user. One of the major problems inherent in this type of system is data sparsity of ratings. This is mainly caused from the underlying similarity measures which produce neighbors based on the ratings records. This paper handles this problem and suggests a new similarity measure. The proposed method takes users rating patterns into account for computing similarity, without just relying on the commonly rated items as in previous measures. Performance experiments of various existing measures are conducted and their performance is compared in terms of major performance metrics. As a result, the proposed measure reveals better or comparable achievements in all the metrics considered.

• 대한수학회지
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• 제49권1호
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• pp.165-185
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• 2012

In this paper, the topological entropy and measure-theoretic entropy for nonautonomous dynamical systems are studied. Some properties of these entropies are given and the relation between them is discussed. Moreover, the bounds of them for several particular nonautonomous systems, such as affine transformations on metrizable groups (especially on the torus) and smooth maps on Riemannian manifolds, are obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권3호
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• pp.522-538
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• 2013

Content based image retrieval has become an increasingly important research topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The retrieval systems rely on a key component, the predefined or learned similarity measures over images. We note that, the similarity measures can be potential improved if the data distribution information is exploited using a more sophisticated way. In this paper, we propose a similarity measure learning approach for image retrieval. The similarity measure, so called Fisher kernel, is derived from the probabilistic distribution of images and is the function over observed data, hidden variable and model parameters, where the hidden variables encode high level information which are powerful in discrimination and are failed to be exploited in previous methods. We further propose a discriminative learning method for the similarity measure, i.e., encouraging the learned similarity to take a large value for a pair of images with the same label and to take a small value for a pair of images with distinct labels. The learned similarity measure, fully exploiting the data distribution, is well adapted to dataset and would improve the retrieval system. We evaluate the proposed method on Corel-1000, Corel5k, Caltech101 and MIRFlickr 25,000 databases. The results show the competitive performance of the proposed method.

• 한국산업정보학회논문지
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• 제6권4호
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• pp.22-29
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• 2001

소프트웨어의 품질 측정의 중요성이 커짐에 따라 새로운 소프트웨어 척도를 개발하기 위한 수많은 노력이 나타났다. 객체 지향 개발 방법론의 중요성에서 볼 때 이러한 경향이 나타나는 특별한 분야는 객체 지향 시스템의 결합도 측정이다. 본 논문에서는 객체 지향 시스템의 결합도를 측정하기 위해 메소드에 의한 응집도 COM과, 클래스에 의한 결합 COC를 제안하였다. 그리고 이를 Briand가 제안한 결합도 성질을 이용하여 평가하고, C++ 언어로 작성된 시스템에 적용하여 다른 결합도 척도와 비교하였다.

• Asia pacific journal of information systems
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• 제16권4호
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• pp.203-215
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• 2006

The advent of KM(knowledge management) concept have led many organizations to seek an effective way to make use of their knowledge. But the absence of right tools for systematic handling of unstructured information makes it difficult to automatically retrieve and share relevant information that exactly meet user's needs. we propose a systematic method to enable content-based information retrieval from corpus of unstructured documents. In our method, a document is represented by using several key terms which are automatically selected based on their quantitative relevancy to the document. Basically, the relevancy is calculated by using a traditional TFIDF measure that are widely accepted in the related research, but to improve effectiveness of the measure, we exploited 'concept network' that represents term-term relationships. In particular, in constructing the concept network, we have also considered relative position of terms occurring in a document. A prototype system for experiment has been implemented. The experiment result shows that our approach can have higher performance over the conventional TFIDF method.

• 한국지능시스템학회논문지
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• 제25권4호
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• pp.386-391
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• 2015

본 논문에서는 한국어와 중국어의 언어학적인 특징을 고려하여 문서 자동분류 시스템의 성능을 높일 수 있는 최적의 자질어 단위를 제안한다. 언어 종속적 단위인 형태소 자질어와 언어 독립적 단위인 n-gram 자질어 그리고 이들을 조합한 복합 자질어 집합을 대상으로 각 언어의 인터넷 신문기사를 SVM으로 분류하는 실험을 수행하였다. 실험 결과, 한국어 문서분류에서는 bi-gram이 F1-measure 87.07%로 가장 좋은 분류 성능을 보였고, 중국어 문서분류에서는 'uni-gram 명사 동사 형용사 사자성어'의 복합 자질어 집합이 F1-measure 82.79%로 가장 좋은 성능을 보였다.

• 지능정보연구
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• 제25권4호
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• pp.89-103
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• 2019

최근 추천시스템의 품질평가 관점에서 이에 대한 다양한 연구들이 진행되고 있다. 추천시스템은 기본적으로 사용자들에게 특정 아이템에 대한 개인화된 추천을 제공하는데 목적이 있으며, 대부분의 추천시스템은 항상 사용자 또는 아이템과 가장 관련 있는 아이템을 추천한다. 그리고 이러한 추천시스템의 성과는 전통적으로 다양한 예측정확도 등에 초점을 두어 왔다. 그러나, 추천시스템은 예측가능성 차원에서 정확해야 할 뿐만 아니라 사용자들에게 유용해야 한다. 특히 최근의 추천시스템에 대한 연구로서, 추천시스템의 평가기준에 속하는, 추천시스템에 대한 사용자 만족도(품질)는 추천시스템이 얼마나 정확하게 추천하느냐 뿐만 아니라 사용자의 의사결정에 얼마나 충분히 도움이 되는지와 관계가 깊다. 예를 들어, 특히 높은 수준의 세렌디티피한 추천은 사용자들이 뜻밖의 아이템이면서 흥미로운 아이템을 찾는데 도움이 된다. 여기서, 세렌디피티란 추천 아이템이 사용자에게 매력적인 동시에 뜻밖의(비기대성의) 아이템인 정도를 의미한다. 본 연구는 추천시스템의 성과를 나타내는 세렌디피티 지표를 추천시스템에 적용하여 추천시스템의 품질을 평가하는 것을 목표로 한다. 본 연구에서는 세렌디피티 지표는 관련성(매력)이 있는 동시에 뜻밖인(비기대성의) 아이템을 추천하는 정도로 정의하고, 이 세렌디피티 지표를 측정하기 위해, 추천시스템이 사용자들에게 예상치 못한 유용한 아이템을 찾을 수(또는 추천할 수) 있는 정도를 평가하였다. 본 연구의 주요 실증분석결과로는, 아이템기반 협력 필터링 기법이 사용자기반 협력 필터링 기법보다 더 높은 세렌디피티값을 가지며, 따라서, 추천시스템의 품질평가 차원에서 아이템기반 협력 필터링 기법은 사용자기반 협력 필터링 기법보다는 더 좋은 추천 품질을 갖고 있음을 보여 주었다.

• 한국정보시스템학회지:정보시스템연구
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• 제12권2호
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• pp.109-128
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• 2003

This study is intended to suggest an ideal direction of the information systems policy for the local small business firms. In an effort to accomplish this objective, this paper tried to identify the current states of the information systems and measure the level of the information systems of local small business firms based on the maturity stage and the development level. The study then explored the evidence how the maturity stage and the development level affect the business performance of the local small business firms. The research found out that the organizational factor such as the support of CEO and the financial factor such as IS investment took major roles to improve the business performance. Based on the research findings of this study, the paper suggested the desirable solution of the information system policy to deal with the problems the local small business firms need to overcome.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권6호
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• pp.1649-1667
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• 2012

In order to improve the accuracy and universality of the flow metric correlation analysis, this paper firstly analyzes the characteristics of Internet flow metrics as random variables, points out the disadvantages of Pearson Correlation Coefficient which is used to measure the correlation between two flow metrics by current researches. Then a method based on Symmetrical Uncertainty is proposed to measure the correlation between two flow metrics, and is extended to measure the correlation among multi-variables. Meanwhile, the simulation and polynomial fitting method are used to reveal the threshold value between different correlation degrees for SU method. The statistical analysis results on the common flow metrics using several traces show that Symmetrical Uncertainty can not only represent the correct aspects of Pearson Correlation Coefficient, but also make up for its shortcomings, thus achieve the purpose of measuring flow metric correlation quantitatively and accurately. On the other hand, reveal the actual relationship among fourteen common flow metrics.

• 천문학회보
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• 제36권1호
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• pp.86.1-86.1
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• 2011

Most astrophysical systems are turbulent and magnetized. Magnetic field plays an important role in the dynamics of ISM and influence all of properties of astrophysical system. Information of magnetic field is very important to understand properties of astrophysical systems. For example, one way to obtain information of magnetic field is to use Rotation Measure. Mean strength of the magnetic field along the line of sight can be estimated from RM/DM. (where RM is rotation measure, DM is dispersion measure) For the estimation of magnetic field strength using RM/DM, the correlation between density and magnetic field is very important. When there is no correlation between density and magnetic field the relation gives exact mean magnetic field strength. But, if the correlation is positive, it overestimates the magnetic field strength, while if the correlation is negative, it underestimate the strength. We calculate correlation between density and magnetic field in compressible MHD turbulence.