• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Korean Journal of Labor Studies
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• v.23 no.2
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• pp.109-139
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• 2017

In light that solidarity movements become significant under the situation of 'labor and labor movement' crisis, our study pays attention to the fact that the Hope Bus campaigns made remarkable achievements, and aims to figure out how those campaigns were successful, by focusing on their solidarity networking. The successful operating conditions of solidarity networking in the Hope Bus campaigns are examined in three aspects - the conditions of triggering, forming, and activating. The solidarity networking of Hope Bus campaigns were mainly triggered by the injustice of layoffs by Hanjin Heavy Industry, aerial protest by Jin-sook Kim, and the tragic symbol of the protest site (Crane no. 85). The solidarity movement of Hope Bus could be formed by the mutual trust and cohesive team-building of key network brokers, their utilization and expansion of social movement networks, and massive ripple effect of SNS-mediated communication. The solidarity networking of Hope Bus was effectively activated by open and de-hierarchical operations of the central planning group, active solidarity activities of participant groups, and the provision of 'heuristic experience' for developing the sensibility to labor solidarity. The virtuous combination of those three operating conditions leads to the building of unified forces among social movements, massive civil participation, and meaningful movement outcomes, through the solidarity networking of Hope Bus campaigns.

• Korean journal of communication and information
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• v.16
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• pp.71-107
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• 2001

미국에서 이루어진 인터넷 중독 현상에 대한 초기 연구는 인터넷 중독을 알코올중독과 같은 개인의 정신적 질병으로 간주하는 의사들에 의해 주도되었다. 그러나 사회현상으로서 인터넷 중독에 대한 사회과학자들의 관심이 증대되면서 인터넷 중독의 원인을 밝히는 본격적인 연구가 이루어진다. 인터넷 이용과 초고속 인터넷 망 보급속도에서 세계최고 수준을 자랑하는 우리의 경우에도 인터넷 이용에 따른 많은 부정적인 현상들이 나타남으로써 사회적인 문제로 대두되고 있다. 이에 따라 인터넷 중독에 대한 일부 연구가 수행되었는데 이들 연구들은 인터넷 이용패턴과 이용동기를 개별적으로 분석하고 인터넷 중독정도를 측정하는 차원에 머물고 있다. 즉, 인터넷 중독의 원인을 분석하는 차원에 이르지 못하고 있다. 또한 대부분의 연구들이 10대 청소년을 연구대상으로 하고 있기 때문에 다른 연령층의 인터넷 이용특성을 파악하는 데 한계를 가지고 있다. 이 같은 현실 인식을 바탕으로 본 연구는 2000년에 발표된 한국전산원 통계수치에서 인터넷을 가장 많이 이용하는 집단으로 조사된 대학생들을 연구대상으로, (1) 이들의 인터넷 이용패턴과 이용동기를 밝히고 (2) 이들 변인들과 인터넷 중독과의 상호관련성을 분석하며 (3) 인터넷 중독의 정도와 중독요인을 조사하고 (4) 마지막으로 인터넷 이용이 다른 미디어 이용과 면대면 커뮤니케이션에 미치는 영향을 분석하고 있다. 본 연구의 자료는 2000년 5월 8일부터 19일까지 2주간에 걸쳐 서울시내 대학생들을 대상으로 강의시간에 설문지를 배포하고 응답자가 설문지에 답하는 방법을 통해 수집되었다. 수집된 556명의 설문지 가운데 유효한 512명의 설문지가 통계적인 방법을 통해 분석되었다. 설문지는 (1) 인터넷 이용패턴 (2) 인터넷 이용 동기 (3) 인터넷 의존도 (4) 인터넷 이용 이후 다른 미디어 이용정도 (5) 인터넷 이용 이후 면대면 커뮤니케이션 정도 (6) 인구통계학적 변인을 측정하는 질문 내용으로 구성되었다. 통계 분석 후 나타난 몇 가지 주요결과를 요약하면 아래와 같다. (1) 이용동기와 인터넷 이용과의 상호관련성 이용동기를 요인 분석한 결과, 6개의 이용동기가 나타났는데 오락이 가장 주요한 동기였으며 다음으로 교육/정보, 현실도피, 외로움, 쇼핑, 그리고 성적 만족 순으로 나타났다. 이용 동기들을 인터넷 이용시간과의 상호관련성을 통계 분석한 결과 기존 연구결과와 달리 성적 만족이 6가지 요인 가운데 가장 낮은 상호관련성을 보였다. 또한 이용동기 분석에서 두 번째 높게 나타난 교육/정보 역시 성적 만족 다음으로 낮은 상호관련성을 보여주었다. 이는 대학생들의 인터넷 이용이 10대들의 인터넷 이용형태와 상당히 다르다는 것을 보여주는 것으로 본 연구에서는 수행하지 못한 이 같은 결과가 나오게 된 이유를 밝히는 후속연구가 필요할 것으로 보인다. (2) 인터넷 이용동기와 인터넷 서비스와의 상호관련성 '오락은 게임, 토론그룹, 전자메일, 채팅과 상호관련을 가진 것으로 나타났으며, 교육/정보는 검색과 쇼핑, 현실도피는 게임과 토론그룹, 외로움은 토론그룹, 전자메일과 채팅, 쇼핑은 온라인 쇼핑과 상호관련성이 있는 것으로 분석되었다. 흥미로운 사실은 성적 만족과 관련해서 게임과 채팅은 긍정적인 상호관련을 가진 것으로 나타난 반면 전자메일 서비스 이용은 성적 만족과 부정적인 상호관련을 가진 것으로 분석되었다. 이는 대학생들이 지루하게 느끼거나 외로움을 느낄 때 전자메일을 주로 이용하지만 성적 만족을 위해 전자메일을 이용하지 않고 있다는 사실을 보여주는 것이다. (3) 인터넷 이용 이후 다른 미디어와 면대면 커뮤니케이션과의 관계 인터넷을 이용한 후 응답자들의 전통적인 미디어(텔레비전, 라디오, 신문, 잡지, 편지, 전화) 이용이 감소되었으며 친구, 가족, 이성친구와의 면대면 커뮤니케이션 역시 감소된 것으로 나타났는데 이 같은 감소가 인터넷 이용과 관련이 있는 것으로 나타났다. (4) 인터넷 중독 정도와 중독 요인 10대들을 대상으로 한 기존 연구에서 나타난 인터넷 중독 현상이 대학생 집단에서는 나타나지 않았다. 그러나 응답자의 28.5%가 중독집단으로 발전될 가능성을 가진 잠재적인 인터넷 의존자(Moderate Internet Dependent)로 조사되었다. 인터넷 중독을 설명하는 요인으로 이용동기 가운데 오락, 외로움과 현실도피가 주요 변인으로 나타났으며 인터넷 이용시간 역시 주요변인으로 분석되었다. 흥미 있는 결과는 선행연구에서 인터넷 중독과 밀접한 관련 있는 인터넷 서비스로 조사된 게임과 채팅이 주요변인으로 나타나지 않았다는 것이다. 또한 인터넷 이용동기와 이용시간과의 상호관련 조사 결과에서처럼 전자메일서비스는 인터넷 중독과 부정적인 관계가 있는 것으로 조사되었다.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.176-188
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• 2018

Modern urban problems are increasingly becoming a market mix that can not be solved by the power of a single department and the necessity of establishing a cooperation system based on data communication between departments is increasing. Therefore, this study analyzed Busan metropolitan city's IT projects from 2014 to 2018 in order to understand the utilization and sharing status of departmental data from the viewpoint that cooperation between departments can start from the sharing of data with high common utilization. In addition, based on the results of the FGI(Focus Group Interview) conducted for the officials of the department responsible for the informatization project, we verified the results of data status analysis. At the same time, we figured out the necessity of data link between departments through SNA(Social Network Analysis) and presented data that should be shared first in the future. As a result, most of the information systems currently use limited data only within the department that produced the data. Most of the linked data was concentrated in the information department. Therefore, this study suggested the following solutions. First, in order to prevent overlapping investments caused by the operation of individual departments and share information, it is necessary to build a small platform to tie the departments, which have high connectivity with each other, into small blocks. Second, a local level process is needed to develop data standards as an extension of national standards in order to expand the information to be used in various fields. Third, as another solution, we proposed a system that can integrate various types of information based on address and location information through application of cloud-based GIS platform. The results of this study are expected to contribute to build a cooperation system between departments through expansion of information sharing with cost reduction.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.123-140
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• 2009

Collaborative filtering recommends products using customers' preferences, so it cannot recommend products to the new customer who has no preference information. This paper proposes a novel approach to new customer recommendations using the social network analysis which is used to search relationships among social entities such as genetics network, traffic network, organization network, etc. The proposed recommendation method identifies customers most likely to be neighbors to the new customer using the centrality theory in social network analysis and recommends products those customers have liked in the past. The procedure of our method is divided into four phases : purchase similarity analysis, social network construction, centrality-based neighborhood formation, and recommendation generation. To evaluate the effectiveness of our approach, we have conducted several experiments using a data set from a department store in Korea. Our method was compared with the best-seller-based method that uses the best-seller list to generate recommendations for the new customer. The experimental results show that our approach significantly outperforms the best-seller-based method as measured by F1-measure.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.229-238
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• 2019

The purpose of this study is to understand in depth the gambling experiences of delinquent juveniles who have dropped out of school and analyze the content and meaning of gambling experiences. Thus, utilized a Giorgi's phenomenological research method. Data was gathered through in-depth interviews, and the gambling experiences of six youths who are under juvenile law protective disposition were examined through a four-step analytic process. From the results, 6 parent components and 18 sub-components were derived. That is, "the temptation of close gambling", "trapped in the bridle of gambling", "deterioration of life", "damaged of non-official networks relationship", "corruption of existence" and "attempts to escape gambling" were appeared. These results are meaningful because they have allowed researchers to identify the essential meaning and experiential structure of delinquents juvenile' gambling experiences outside of school, which has not been studied thus far. Further, this study has empirical value as its framework includes a joint analysis in terms of welfare and gambling addiction and focuses on a precarious population-delinquent juveniles who have dropped out of school. The results provide a foundation from which practical implications and policy alternatives for the prevention and treatment of gambling problems in delinquent juveniles dropped out of school.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.109-135
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• 2021

his study classified the development process of artificial intelligence (AI) speakers through analysis of the news text of artificial intelligence (AI) speakers shown in traditional news reports, and identified the characteristics of each product by period. The theoretical background used in the analysis are news frames and topic frames. As analysis methods, topic modeling and semantic network analysis using the LDA method were used. The research method was a content analysis method. From 2014 to 2019, 2710 news related to AI speakers were first collected, and secondly, topic frames were analyzed using Nodexl algorithm. The result of this study is that, first, the trend of topic frames by AI speaker provider type was different according to the characteristics of the four operators (communication service provider, online platform, OS provider, and IT device manufacturer). Specifically, online platform operators (Google, Naver, Amazon, Kakao) appeared as a frame that uses AI speakers as'search or input devices'. On the other hand, telecommunications operators (SKT, KT) showed prominent frames for IPTV, which is the parent company's flagship business, and 'auxiliary device' of the telecommunication business. Furthermore, the frame of "personalization of products and voice service" was remarkable for OS operators (MS, Apple), and the frame for IT device manufacturers (Samsung) was "Internet of Things (IoT) Integrated Intelligence System". The econd, result id that the trend of the topic frame by AI speaker development period (by year) showed a tendency to develop around AI technology in the first phase (2014-2016), and in the second phase (2017-2018), the social relationship between AI technology and users It was related to interaction, and in the third phase (2019), there was a trend of shifting from AI technology-centered to user-centered. As a result of QAP analysis, it was found that news frames by business operator and development period in AI speaker development are socially constituted by determinants of media discourse. The implication of this study was that the evolution of AI speakers was found by the characteristics of the parent company and the process of co-evolution due to interactions between users by business operator and development period. The implications of this study are that the results of this study are important indicators for predicting the future prospects of AI speakers and presenting directions accordingly.

• Journal of Science and Technology Studies
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• v.18 no.2
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• pp.181-232
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• 2018

To explore the history of Korean engineers, one must navigate the field answering to the question of who engineers are. This is not an easy task because, behind the English expression of engineer, there are various names and even histories pertaining to technology related actors in East Asian countries including South Korea, and the meaning and status of these names are different from one another. Thus, the process and method of answering to the question of who engineers are becomes the path to understanding the history of Korean engineers. This study, therefore, attempts to suggest research questions that should be raised to study Korean engineers and to find research methodology suited for addressing those questions. Until now, not enough efforts have been made to create and expand interdisciplinary discussions and contacts for this area of study. This study has some theoretical difficulties of having to combine concepts with different problematique. Nevertheless, it aims to discuss how to conduct research, what questions should be posed to analyze the construction of Korean engineers and what research methodologies are suitable for such research, based on previous researches conducted in the field of social science. Answers to the quest are sought through genealogy, conceptual history, actor-network theory, and the notion of techno-national formation.

• Korean Association of Arts Management
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• no.58
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• pp.205-239
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• 2021

This study explores the importance of semantic web-based network construction in art data archiving, as well as its meaning and value in the context of arts management along with its potential for future application. The study focuses on oral history obtained from the Arko Arts Archives that contained records of the lives and artistic views of early artists. In this study, the possibility of applying semantic web-based technology to materials concerning culture and the arts was discussed in five aspects based on the results of the case analysis. First, checking the relationship and discovering hidden artists are possible by revealing relationships between characters. Second, understanding and studying society and culture at a given time is possible by interpreting the contextual meaning of information. Third, art exploration can be done broadly and deeply, encompassing various genres from the perspective of the consumer. Fourth, through art construction, history can be reconstructed using a new and rich method. Fifth, expanding the scope beyond the boundaries of art is possible through convergence and collaboration of programs that handle big data. The network data can be used in various methods, such as art history research, art planning, and creation, throughout the art ecosystem. The results of the study suggest that digitizing a large quantity of data concerning culture and the arts is meaningful in arts management as well as identifying and analyzing the relationship network among data clusters using semantic web-based technology.