• Journal of Life Science
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• v.34 no.7
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• pp.443-452
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• 2024

This study aimed to examine the influence of 3,6-anhydroxygalactose (L-AHG) on the pro-inflammatory M1 polarization and pro-inflammatory responses observed in the RAW264.7 mouse macrophage cell line following stimulation with lipopolysaccharides (LPS). L-AHG exhibited a significant and dose-dependent inhibition of inducible nitric oxide synthase (iNOS) expression, a hallmark of M1 polarization, and subsequent NO production in LPS-stimulated RAW264.7 cells. Furthermore, the LPS-induced upregulation of cyclooxygenase-2 (COX-2), which drives the production of prostaglandin E2, an inflammatory mediator, was also inhibited by L-AHG. L-AHG did not affect the LPS-triggered Toll-like receptor 4 (TLR4)-mediated pro-inflammatory signaling pathway, which culminated in the activation of transforming growth factor-β-activated kinase 1 (TAK1). However, it was observed to inhibit the generation of reactive oxugen species (ROS) in a dose-dependent manner, as well as the TAK1-driven activation of JNK and p38 MAPK. Given that the active p38 MAPK is known to contribute to the assembly of active nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, which catalyzes the intracellular generation of pro-inflammatory ROS in LPS-stimulated macrophages, the dose-dependent reduction in the LPS-induced ROS generation by L-AHG may be mainly due to the prevention of TAK1-driven activation of p38 MAPK. Together, these results demonstrate that the L-AHG-mediated inhibition of the TAK1-JNK/p38 MAPK activation phase of the pro-inflammatory signaling pathway in LPS-stimulated RAW264.7 cells by L-AHG represents a promising mechanism for suppressing M1 polarization and pro-inflammatory responses in macrophages.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.107-119
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• 2024

The purpose of this study is to examine and analyze the vocabulary and problem-solving ability characteristics of adolescents with developmental disabilities related to leisure and recreation and use them as basic data in education and support of recreation activities for adolescents with developmental disabilities. The study participants were comprised of adolescents with developmental disabilities, divided into two groups based on their receptive language age: those under 10 years old and those 10 years and older. The results obtained through this study are as follows. First, there was a significant difference in leisure and recreation vocabulary between the two groups according to receptive language age. Second, there was a significant difference in problem-solving ability between the two groups based on their receptive language age. Third, the analysis of the correlation between leisure and recreation vocabulary and problem-solving abilities within each group revealed that the under 10 years old group showed the highest correlation in basic vocabulary and basic problem-solving abilities, while the 10 years and older group exhibited the highest correlation in intermediate and advanced levels of problem-solving abilities. Fourth, the analysis of incorrect responses to leisure and recreation vocabulary showed a high rate of selecting vocabulary related to similar topics as incorrect answers. Additionally, the analysis of overreactions to problem-solving abilities indicated an increasing tendency of incorrect responses in items requiring context comprehension. Additionally, the analysis of incorrect responses to problem-solving abilities indicated a tendency of higher error rates in items requiring context comprehension. The results of this study provide insights for discussing directions in communication-related skills education for the smooth recreation life of adolescents with developmental disabilities. Accordingly, it is expected to be utilized as foundational information for educational and support programs aimed at the successful recreation activities of adolescents with developmental disabilities.

• Information Systems Review
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• v.25 no.2
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• pp.145-162
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• 2023

Asset management is a complex and difficult field that requires insight into numerous variables and even human psychology. Thus, it has traditionally been the domain of professionals, and these services have been expensive to obtain. Changes are taking place in these markets, and the driving force is the digital revolution, so-called the fourth industrial revolution. Among them, the Robo-Advisor service using artificial intelligence technology is the highlight. The reason is that it is possible to popularize investment advisory services with convenient accessibility and low cost. This study aims to clarify what factors are critically important when selecting robo-advisors for service users and providers in Korea, and what perception differences exist in the selection factors between user and provider groups. The framework of the study was based on the marketing mix 4C model, and the design and analysis of the model used Delphi survey and AHP. Through the study design, 4 main criteria and 15 sub-criteria were derived, and the findings of the study are as follows. First, the importance of the four main criteria was in the order of customer needs > customer convenience > customer cost > customer communication for both groups. Second, looking at the 15 sub-criteria, it was found that investment purpose coverage, investment propensity coverage, fee level and accessibility factors were the most important. Third, when comparing between groups, the user group found that the fee level and accessibility factors were the most important, and the provider group recognized the investment purpose coverage and investment propensity coverage factors as important. This study derived useful implications in practice. First, when designing for the spread of the robo-advisor service, the basis for constructing a user-oriented system was prepared by considering the priority of importance according to the weight difference between the four main criteria and the 15 sub-criteria. In addition, the difference in priority of each sub-criteria shown in the group comparison and the cause of the sub-criteria with large weight differences were identified. In addition, it was suggested that it is very important to form a consensus to resolve the difference in perception of factors between those in charge of strategy and marketing and system development within the provider group. Academically, it is meaningful in that it is an early study that presented various perspectives and perspectives by deriving a number of robo-advisor selection factors. Through the findings of this study, it is expected that a successful user-oriented robo-advisor system can be built and spread in Korea to help users.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.53-77
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• 2015

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.

• Korean Business Review
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• v.21 no.2
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• pp.137-170
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• 2008

This study is presenting the logistics strategy in the international logistics markets which makes competition and corporation among north-east Asian countries to establishing the multi-pass Eurasian railroads. The countries located in north-east area of Eurasia like China, Japan, Russia and Korea are paying higher costs and disutility to the transportations and communications due to repeated conflicts and confrontations causes from the politic problems. They are being used surface transportation for most of all logistics between Europe and Asia except special merchandises because of characteristic of cargo to be air, the Silk Road remains vestige only which was main logistic passage to this area since BC. So far the Trans-Siberian Railway is being used by Russia mostly as north of Eurasian transport because of difficulties of service. The Trans-China Railway built in 1992 is not accomplishing as a international logistic passages. It is expected to take a long lead time because of characteristic of resource development and poor logistic infrastructure to the countries like Uzbekistan, double landlocked country, Mongolia and Azerbaijan, the countries do not be adjacent to the sea, even they have great economic jump-up plans through the development of their own resources. The Shanghai Cooperation Organization(SCO) start to sail officially in 2001 is constructed with China, Russia, Tadzhikistan, Kyrgyzstan, Kazakhstan and Uzbekistan as regular members of 6 countries and Mongolia, India, Pakistan, Afghanistan and Iran as observers 5 countries. It is started as a military alliance to protect terror, but now, it is expended to cooperate with the traffic, transportation, trade and share of energies. The Russia is doing their best to activate TSR as a government target to developnorth area equivalently, and economic develop of far-east Siberia. And also it is agreed provisionally to improve and repair of rail road between Nahjin and Hassan to connect TSR and TKR( Trans-Korea Railroad) by Russia, North Korea and South Korea with Russian's aggressive efforts. The development plan of this area is over lapped with GTI(Greater Tumen Initiative) promoted by UNDP, and is a cooperated project by 5 countries of South Korea, Mongolia, China, Russia and North Korea, subject to review the appropriation of energy, tour, environment, rail road connection between Mongolia and China and establishing a ferry route to north-east Asia. It is Japanese situation to pay attention to Russia and China even they have been supplying large-scope of infrastructure in Mongol area without any charges, target to get East Asia Main Rail Road to connect Mongolia and Zalubino of Russia. In case of the program for the Denuclearization of North Korea is not creeping, it will be accelerated to connect the TKR and TSR, TKR and TCR by somehow attending United States, including developing program promoted by UN ESCAP. As the result, Korean peninsular will continue the central role of competition and cooperation as in the past, now and future of north-east Asia, as of geographical-economics and geographical-politics whether it is requested or not wanted by neighbor countries.

• Tuberculosis and Respiratory Diseases
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• v.44 no.5
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• pp.1094-1104
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• 1997

Background : Spontaneous pneumothoraces(SP) are divided into primary spontaneous pneumothoraces (PSP) which develop in healthy individuals without underlying pulmonary disorders and secondary spontaneous pneumothoraces(SSP) which occur in those who have underlying disorders such as tuberculosis or chronic obstructive lung diseases. Yet there is no established standard therapeutic approach to this disorder, i.e., from the spectrum of noninvasive treatment such as clinical observation with or without oxygen therapy, to aggressively invasive thoracoscopic bullectomy or open thoracotomy. Although chest tube thoracostomy has been most widely used, the patients should overcome pain in the initiation of tube insertion or during indwelling it potential infection and subcutaneous emphysema. Thus smaller-caliber tube has been challenged for the treatment of pneumothorax. Previously, we studied the therapeutic efficacy of 8 French catheter for spontaneous pneumothorax. But there has been few data for effectiveness of small-caliber catheterization in comparison with that of chest tube. In this study, we intended to observe the long-term effectiveness of 8 French catheter for the treatment of spontaneous pneumothoraces in comparison with that of chest tube thoracostomy. Method : From January, 1990 to January, 1996, sixty two patients with spontaneous pneumothoraces treated at Chung-Ang University Hospital were reviewed retrospectively. The patients were sub-divided into a group treated with 8 French catheter(n=23) and the other one with chest tube insertion(n=39). The clinical data were reviewed(age, sex, underlying pulmonary disorders, past history of pneumothorax, size of pneumothorax, follow-up period). And therapeutic effect of two groups was compared by treatment duration(duration of indwelling catheter or tube), treatment-associated complications and recurrence rate. Results : The follow-up period(median) of 8 French catheter group and chest tube group was 28 and 22 months, which had no statistical significance. Ther was no statistically significant difference of clinical characteristics between two groups with SP, PSP, SSP. The indwelling time of 8 French catheter group was $6.2{\pm}3.8$ days, which was significantly shorter than that of chest tube group in SP, $9.1{\pm}7.5$ days(p=0.047). In comparison of treatment-related complication in PSP, 8 French catheter group as 6.25% of complication showed lower tendency than the other group as 23.8% (p=0.041 ; one-tailed, p=0.053; two-tailed). The recurrence rate in each group of SP was 17.4%, 10.3%, which did not show any statistically significant difference. Conclusion : Treatment with 8 French catheter resulted in shorter indwelling time in sponteous pneumothorax, and lower incidence of treatment-related complication in primary spontaneous pneumothorax. And the recurrence rate in each of treatment group showed no statistically significant difference. So, we can recommend the 8 French small-caliber catheter for the initial therapy for spontaneous pneumothorax for the replacement of conventional chest tube thoracostomy. But further prospective study with more subjects of spontaneous pneumothorax will be needed for the evaluation of effectiveness of 8 French cateter.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.57-79
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• 2016

It is a rising trend that the number of users using one of the social media channels, the Social Network Service, so called the SNS, is getting increased. As per to this social trend, more companies have interest in this networking platform and start to invest their funds in it. It has received much attention as a tool spreading and expanding the message that a company wants to deliver to its customers and has been recognized as an important channel in terms of the relationship marketing with them. The environment of media that is radically changing these days makes possible for companies to approach their customers in various ways. Particularly, the social network service, which has been developed rapidly, provides the environment that customers can freely talk about products. For companies, it also works as a channel that gives customized information to customers. To succeed in the online environment, companies need to not only build the relationship between companies and customers but focus on the relationship between customers as well. In response to the online environment with the continuous development of technology, companies have tirelessly made the novel marketing strategy. Especially, as the one-to-one marketing to customers become available, it is more important for companies to maintain the relationship marketing with their customers. Among many SNS, Facebook, which many companies use as a communication channel, provides a fan page service for each company that supports its business. Facebook fan page is the platform that the event, information and announcement can be shared with customers using texts, videos, and pictures. Companies open their own fan pages in order to inform their companies and businesses. Such page functions as the websites of companies and has a characteristic of their brand communities such as blogs as well. As Facebook has become the major communication medium with customers, companies recognize its importance as the effective marketing channel, but they still need to investigate their business performances by using Facebook. Although there are infinite potentials in Facebook fan page that even has a function as a community between users, which other platforms do not, it is incomplete to regard companies' Facebook fan pages as communities and analyze them. In this study, it explores the relationship among customers through the network of the Facebook fan page users. The previous studies on a company's Facebook fan page were focused on finding out the effective operational direction by analyzing the use state of the company. However, in this study, it draws out the structural variable of the network, which customer committment can be measured by applying the social network analysis methodology and investigates the influence of the structural characteristics of network on the business performance of companies in an empirical way. Through each company's Facebook fan page, the network of users who engaged in the communication with each company is exploited and it is the one-mode undirected binary network that respectively regards users and the relationship of them in terms of their marketing activities as the node and link. In this network, it draws out the structural variable of network that can explain the customer commitment, who pressed "like," made comments and shared the Facebook marketing message, of each company by calculating density, global clustering coefficient, mean geodesic distance, diameter. By exploiting companies' historical performance such as net income and Tobin's Q indicator as the result variables, this study investigates influence on companies' business performances. For this purpose, it collects the network data on the subjects of 54 companies among KOSPI-listed companies, which have posted more than 100 articles on their Facebook fan pages during the data collection period. Then it draws out the network indicator of each company. The indicator related to companies' performances is calculated, based on the posted value on DART website of the Financial Supervisory Service. From the academic perspective, this study suggests a new approach through the social network analysis methodology to researchers who attempt to study the business-purpose utilization of the social media channel. From the practical perspective, this study proposes the more substantive marketing performance measurements to companies performing marketing activities through the social media and it is expected that it will bring a foundation of establishing smart business strategies by using the network indicators.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.