• Journal of the Korean housing association
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• v.20 no.6
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• pp.47-56
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• 2009

Housing Performance Rating Indication System was started in 2006. The aim of Housing Performance Rating Indication is to provide the guideline of housing performance. Repair Convenience is similar to Remodeling. Remodeling has been paid more attention as an alternative for reconstruction. Especially, now the actual house supply is facing almost 100%, it is time to keep and maintenance management of apartment buildings, and ultimately it will expand the Long life housing. Especially consumers has to have a right to select apartment buildings by comparing performance of house. For construction companies, they need a performance code that standardized by government, so they can provide a Certain performing house. This study examines data which has been obtained from the recent application of Housing Performance Rating Indication System and Repair Convenience category is main concern. Findings from the study will provide vital information in improving current Housing Performance Rating Indication System.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.12
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• pp.41-49
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• 2018

The current housing performance rating system displays the performance rating of the houses supplied by the housing construction company, approved by the Housing Law. This system is intended to provide accurate information to the consumers in the announcement of the tenant recruitment, thereby improving the quality of the housing. This system was revised in January 2005 and implemented from January 2006. It is obligatory for companies with a scale of more than 1,000 households (energy performance rating is over 300 households), but there is currently no content on the waterproofing field. Therefore, this study suggests the revision plan proposes the necessity of the inclusion of the performance grade for the waterproofing field which is not currently implemented in the apartment house performance rating system, and to understand how to secure an adequate living environment. As a result, according to survey results, the performance score based on the difficulty of the maintenance method according to future leaks was selected and the revised system was prepared. Based on this, the performance grade for the virtual apartment was evaluated, and the results show that the performance of various grades is presented accordingly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2602-2608
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• 2013

User-based and item-based method have been developed as the solutions of the movie recommendation problem. However, these methods are faced with the sparsity problem and the problem of not reflecting user's rating respectively. In order to solve these problems, there is a research on the combination of the two methods using the concept of similarity. In reality, it is not free from the problem of sparsity, since it has a lot of parameters to be calculated. In this study, we propose a recommendation method using rating difference between items in order to complement this problem. This method is relatively free from the problem of sparsity, since it has less parameters to be calculated. And it can get more accurate results by reflecting the users rating to calculate the parameters. In experiments for the proposed method, the initial error is large, but the performance has been quickly stabilized after. In addition, it showed a 0.0538 lower average error compared to the existing method using similarity.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.347-351
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• 2019

The purpose of this study is to suggest the Assessment model for tourist information center in Seoul. As a research method, we analyzed international guideline and interview with tourism experts in order to rate the tourist centers in Seoul. Secondly, we renamed the international rating model to Itaewon information center as a typical landmark in Seoul. The assessment factors for T.I.C is assembled through researching of the centers' status in terms of overall service satisfaction. Via in - depth interview with 9 visitors, as a result, we were able to derive the possibility that new-designed rating model is able to be applied to the Tourist centers in Seoul. It is significant that this study suggests ways to improve domestic tourist center service. It is expected that the follow - up study will help improve the factors to Seoul T.I.C, not only Itaewon, with much more specific rating method.

• Ocean and Polar Research
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• v.26 no.2
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• pp.219-230
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• 2004

The aims of this study are to construct database using geostatistics and Geographic Information System (GIS), and to derive the spatial relationships between manganese nodule abundance and each factor affecting nodule abundance, such as metal grade, slope, aspect, water depth, topography, and acoustic characteristics of the subbottom using the GIS and probability methods. The greater is the copper and nickel grade, the higher is the rating. The distribution pattern of nickel grade is similar to that of copper grade. The slopes are generally less than $3^{\circ}$, excluding seamounts and cliff areas. There is no increment in the rating with increasing slope. The rating is highest for slopes between 2.5 and $3.5^{\circ}$ in block B2 and between 3 and $6^{\circ}$ in block C1. The topography is classified into five groups: seamount, hill crest, hill slant, hill base or plain, and seafloor basin or valley. The ratings prove lowest for seamount and hill crest. The results of the study show a decrease in the rating with an increase in water depth in the study area. There was a poor relationship between manganese nodule abundance and the thickness of the upper transparent layer in block C1. Using GIS, it is possible to analyze a large amount of data efficiently, and to maximize the practical application, to increase specialization, and to enhance the accuracy of the analyses.

• The Journal of Information Systems
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• v.29 no.3
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• pp.145-157
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• 2020

Purpose This study proposed an approach for predicting the efficiency rating of the cultural tourism festivals using DEA and machine learning techniques. The cultural tourism festivals are selected for the best festivals through peer reviews by tourism experts. However, only 10% of the festivals which are held in a year could be evaluated in the view of effectiveness without considering the efficiency of festivals. Design/methodology/approach Efficiency scores were derived from the results of DEA for the prediction of efficiency ratings. This study utilized BCC models to reflect the size effect of festivals and classified the festivals into four ratings according the efficiency scores. Multi-classification method were considered to build the prediction of four ratings for the festivals in this study. We utilized neural networks and SVMs with OAO(one-against-one), OAR(one-against-rest), C&S(crammer & singer) with Korea festival data from 2013 to 2018. Findings The number of total visitors in low efficient rating of DEA is more larger than the number of total visitors in high efficient ratings although the total expenditure of visitors is the highest in the most efficient rating when we analyzed the results of DEA for the characteristics of four ratings. SVM with OAO model showed the most superior performance in accuracy as SVM with OAR model was not trained well because of the imbalanced distribution between efficient rating and the other ratings. Our approach could predict the efficiency of festivals which were not included in the review process of culture tourism festivals without rebuilding DEA models each time. This enables us to manage the festivals efficiently with the proposed machine learning models.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.83-89
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• 2022

As a representative technique of recommender systems, collaborative filtering has been successfully in service through many commercial and academic systems. This technique recommends items highly rated by similar neighbor users, based on similarity of ratings on common items rated by two users. Recently research on time-aware recommender systems has been conducted, which attempts to improve system performance by reflecting user rating time of items. However, the decay rate uniform to past ratings has a risk of lowering the rating prediction performance of the system. This study proposes a rating time-aware similarity measure between users, which is a novel approach different from previous ones. The proposed approach considers changes of similarity value over time, not item rating time. In order to evaluate performance of the proposed method, experiments using various parameter values and types of time change functions are conducted, resulting in improving prediction accuracy of existing traditional similarity measures significantly.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.85-100
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• 2023

Corporate ESG performance (environmental, social, and corporate governance) reflecting a company's strategic sustainability has emerged as one of the main factors in today's investment decisions. The traditional ESG performance rating process is largely performed in a qualitative and subjective manner based on the institution-specific criteria, entailing limitations in reliability, predictability, and timeliness when making investment decisions. This study attempted to predict the corporate ESG rating through automated machine learning based on quantitative and disclosed corporate financial information. Using 12 types (21,360 cases) of market-disclosed financial information and 1,780 ESG measures available through the Korea Institute of Corporate Governance and Sustainability during 2019 to 2021, we suggested a deep neural network prediction model. Our model yielded about 86% of accurate classification performance in predicting ESG rating, showing better performance than other comparative models. This study contributed the literature in a way that the model achieved relatively accurate ESG rating predictions through an automated process using quantitative and publicly available corporate financial information. In terms of practical implications, the general investors can benefit from the prediction accuracy and time efficiency of our proposed model with nominal cost. In addition, this study can be expanded by accumulating more Korean and international data and by developing a more robust and complex model in the future.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.189-199
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• 2005

Technology has many potential uses and changes by social context surrounding it. Therefore political implications are reflected in the internal logical system of articraft, technology. This explanation is ale to apply to the regulation technology of Internet. The empowerment of information is changeable according as which regulation technology is adopted. This paper explores the structure, principle and its social implication of regulation technology of Internet, which coincides with user empowerment of information as a case of ICRS(Internet Content Rating System).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3627-3641
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• 2021

Fintech, which stands for financial technology, is growing fast globally since the economic crisis hit the United States in 2008. Fintech companies are striving to secure a competitive advantage over existing financial services by providing efficient financial services utilizing the latest technologies. Fintech companies can be classified into several areas according to their business solutions. Among the Fintech sector, peer-to-peer (P2P) lending companies are leading the domestic Fintech industry. P2P lending is a method of lending funds directly to individuals or businesses without an official financial institution participating as an intermediary in the transaction. The rapid growth of P2P lending companies has now reached a level that threatens secondary financial markets. However, as the growth rate increases, so does the potential risk factor. In addition to government laws to protect and regulate P2P lending, further measures to reduce the risk of P2P lending accidents have yet to keep up with the pace of market growth. Since most P2P lenders do not implement their own credit rating system, they rely on personal credit scores provided by credit rating agencies such as the NICE credit information service in Korea. However, it is hard for P2P lending companies to figure out the intentional loan default of the borrower since most borrowers' credit scores are not excellent. This study analyzed the voices of telephone conversation between the loan consultant and the borrower in order to verify if it is applicable to determine the personal credit score. Experimental results show that the change in pitch frequency and change in voice pitch frequency can be reliably identified, and this difference can be used to predict the loan defaults or use it to determine the underlying default risk. It has also been shown that parameters extracted from sample voice data can be used as a determinant for classifying the level of personal credit ratings.