• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.235-241
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• 2015

Nowadays, research and industry on the internet of things is rapidly developing. Bug fixed field of the Software development related internet of things is a very important things. In this study, we analyze the properties that can affect what the bug fix-time by analyzing the time required to fix a bug associated with the Internet of Things. Using the k-NN classification method based on the attribute information to be classified as bug reports. Extracts a bug report based on the results of a similar property. Bug fixed by calculating the time of a similar bug report predicts the fix-time for new bugs. Depending on the prediction of the properties that affect the bug correction time, the properties of os, component, reporter, and assignee showed the best prediction accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.1009-1016
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• 2015

In this paper, we propose EPCM(Efficient Prediction-based Context-awareness Matrix) algorithm analyzing connectivity by predicting cluster's context data such as velocity and direction. In the existing DTN, unrestricted relay node selection causes an increase of delay and packet loss. The overhead is occurred by limited storage and capability. Therefore, we propose the EPCM algorithm analyzing predicted context data using context matrix and adaptive revision weight, and selecting relay node by considering connectivity between cluster and base station. The proposed algorithm saves context data to the context matrix and analyzes context according to variation and predicts context data after revision from adaptive revision weight. From the simulation results, the EPCM algorithm provides the high packet delivery ratio by selecting relay node according to predicted context data matrix.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.57-66
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• 2017

The academic research on data governance is still in its infancy and focused on the definition of concept and components. However, we need to study of evaluation on data governance to help make decision of establishment. The purpose of this paper is to develop of attribute index in data governance framework. Therefore, in this paper, we used RGT (repertory grid technique) and Laddering techniques for experts interview and survey for validation of disinterested third party experts and analysis statistically. We completed data governance attribute index which is composed of data compliance area including 8 components, data quality area including 16 components and data organization area including 7 components. Moreover, the evaluation attributes is prioritized and ranked using the AHP. As a result of the study, this paper can be used for the base line data in introducing and operating data governance in an IT company.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.924-934
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• 2014

In the last 10 years the digital music contents market has grown rapidly. However digital music contents product and services are not managed with product planning and price policy considered customer attitude and digital music contents values. This study is to define the value properties of digital music contents services based on streaming and download as genre, price, sound quality, and usage appliance, and suggest the strategic market price and service composition of digital music contents service by customer attitudes about the value properties. The research used the conjoint analysis methodology based on the hedonic price model and collected 405 questionaries by users of Korean digital music contents services to the analysis. Hence 'sound quality' in download platform, and 'appliance' in streaming platform were the elements to evaluate the customer attitude. The results present the music contents productions and companies have to provide the differentiated services and price by the value properties of user preference in the market.

• Journal of Digital Convergence
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• v.11 no.4
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• pp.147-152
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• 2013

Now, plastic surgery has become the industry for beauty. In order to know the characteristics of high-visit web sites that many people have visited, 33 high visit websites of plastic surgery were compared to 60 benchmark sites of same industry. We selected 34 web site attributes that can be measured objectively from existing studies. For analysis, Multiple Discriminant Analysis(MDA) is conducted for searching what attributes divide two group definitely. The result of this study shows the dividing attributes fall into 2 categories like 'Community', 'Up to date'. Thus, we are able to conclude that high-visit plastic surgery web sites are community-centric site but not contents-centric and are maintained with tide up to date. The methodology employed in this study provides an efficient way of improving satisfaction of visitors of plastic surgery website.

• Journal of Global Scholars of Marketing Science
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• v.15 no.3
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• pp.149-168
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• 2005

The purpose of this paper is to study the impact of brand personality on the choice of tour site. For this purpose, Japanese, Chinese and Korean tourists visiting Jeju-Ireland were sampled and asked to evaluate the personality dimensions and attributes of six major tour sites in Asia. Factor analysis is applied to 42 personality scales of Aaker and 5 personality dimensions are extracted. Then, Multinomial Logit model is applied to estimate the relative impact of personality dimensions and attributes on the choice of tour sites. Results suggest useful implications. The personality of tour sites has meaningful influence on choice of tour sites, in some cases more important than tour site attributes. Among 5 dimensions of personality, sincerity and excitement are found to be important dimensions in the choice process of tour site. Sophistication of the site, expressed as glamorous, charming, handsomeness, uniqueness, and smooth, is also found to be important in determining intention to visit in the future.

• Journal of Korea Game Society
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• v.18 no.1
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• pp.105-114
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• 2018

Various data analysis methods used for customer type analysis are very important for game companies to understand their type and characteristics in an attempt to plan customized content for our customers and to provide more convenient services. In this paper, we propose a k-mode cluster analysis algorithm that uses information uncertainty by extending information entropy to reduce information loss. Therefore, the measurement of the similarity of attributes is considered in two aspects. One is to measure the uncertainty between each attribute on the center of each partition and the other is to measure the uncertainty about the probability distribution of the uncertainty of each property. In particular, the uncertainty in attributes is taken into account in the non-probabilistic and probabilistic scales because the entropy of the attribute is transformed into probabilistic information to measure the uncertainty. The accuracy of the algorithm is observable to the result of cluster analysis based on the optimal initial value through extensive performance analysis and various indexes.

• Management & Information Systems Review
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• v.32 no.1
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• pp.43-62
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• 2013

In order to know the characteristics of high visit web sites that many people have visited, 37 high visit websites of plastic surgery were compared to 69 benchmark sites of same industry. We selected 36 web site attributes that can be measured objectively from existing studies and composed the data set of 36 attributes multiplied by 106 websites. For analysis, Multiple Discriminant Analysis(MDA) and Decision Tree Technique are conducted for searching what attributes divide two group definitely. The result of this study shows the dividing attributes fall into 3 categories like 'Community', 'Mobile', 'Up to date'. Thus, we are able to conclude that high visit plastic surgery web sites are community centric site but not contents centric, response a change to mobile environment rapidly and are maintained with tide up to date. The methodology employed in this study provides an efficient way of improving satisfaction of visitors of plastic surgery website.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.183-194
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• 1999

The Brightness, color and texture included in a multispectral satellite data are used as important factors to analyze and to apply the image data for a proper use. One of the most significant process in the satellite data analysis using texture or color information is to extract features effectively expressing the information of original image. It was described in this paper that six features were introduced to extract useful features from the analysis of the satellite data, and also a classification network using the back-propagation neural network was constructed to evaluate the classification ability of each vector feature in SPOT imagery. The vector features were adopted from the training set selection for the interesting region, and applied to the classification process. The classification results showed that each vector feature contained many merits and demerits depending on each vector's characteristics, and each vector had compatible classification ability. Therefore, it is expected that the color and texture features are effectively used not only in the classification process of satellite imagery, but in various image classification and application fields.