• Journal of Information Processing Systems
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• v.15 no.3
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• pp.616-631
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• 2019

With the rapid increase of information on the World Wide Web, finding useful information on the internet has become a major problem. The recommendation system helps users make decisions in complex data areas where the amount of data available is large. There are many methods that have been proposed in the recommender system. Collaborative filtering is a popular method widely used in the recommendation system. However, collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we propose a movie recommendation system by using social network analysis and collaborative filtering to solve this problem associated with collaborative filtering methods. We applied personal propensity of users such as age, gender, and occupation to make relationship matrix between users, and the relationship matrix is applied to cluster user by using community detection based on edge betweenness centrality. Then the recommended system will suggest movies which were previously interested by users in the group to new users. We show shown that the proposed method is a very efficient method using mean absolute error.

• Korea Trade Review
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• v.47 no.3
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• pp.75-92
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• 2022

This study aims to analyze the International trade network of Water transport service using Social Network Analysis for defining the status of Korean Water transport industry. This study use World Input-Output Table of Asian Development Bank from 2000 to 2020 and build the International trade matrix of Water transport service from that. Therefore, this study analyze Out-degree centrality, In-degree centrality and betweenness centrality of Korea and other main countries in the matrix of World Water transport industry. As a result, Korea rank above 10th in the all centralities and the total output also rank 8th in the world, therefore, this study show the importance of Korean Water transport industry in the world. However, Singapore has the highest centrality in the world, even though China has the largest Total output among 63 countries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.313-318
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• 2016

The recommendation service is changing from client-server based internet service to social networking. Especially in recent years, it is serving recommendations with personalization to users through crowdsourcing and social networking. The social networking based systems can be classified depending on methods of providing recommendation services and purposes by using memory and model based collaborative filtering. In this study, we proposed the social network based sensibility design recommendation using associative user. The proposed method makes {user - associative design} matrix through the social network and recommends sensibility design using the memory based collaborative filtering. For the performance evaluation of the proposed method, recall and precision verification are conducted. F-measure based on recommendation of social networking is used for the verification of accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.241-248
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• 2018

A Recommender System (RS) is a system that provides optimized information to users in an over-supply situation. The key to RS is to accurately predict the behavior of the user. The Matrix Factorization (MF) method was used for this prediction in the early stage, and according to the recent SNS development, social information is additionally utilized to improve prediction accuracy. In this paper, we use RS internal trust cluster, which was overlooked in previous studies, to further improve performance and analyze the characteristics of trust clusters.

• Korea Trade Review
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• v.47 no.4
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• pp.125-135
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• 2022

This study is to analyze the economic effects of tariff reduction using computable general equilibrium(CGE) model. We set up the social accounting matrix for five-base equilibrium year. Our main findings are as follows. First, the impact of tariff reduction on GDP was different from time to time. It meas that the differentiated economics structure was affected by tariff reduction. As our economic grew up, the impact of tariff reduction was measured much higher. Second, until 1995 the impact of tariff reduction on total export and import was increased, then while 1995 the increase was dropped. This is because we reduced the tariff by the WTO negotiations. Third, the tariff reduction affected the price of imported goods, so it contributed to substitute effects between domestic and imported goods. According to these results, we found out the importance of the linkage between the tariff reduction and economic structure.

• Journal of applied mathematics & informatics
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• v.28 no.3_4
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• pp.569-582
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• 2010

The following "Periodic Jacobi Procrustes" problem is studied: find the Periodic Jacobi matrix X which minimizes the Frobenius (or Euclidean) norm of AX - B, with A and B as given rectangular matrices. The class of Procrustes problems has many application in the biological, physical and social sciences just as in the investigation of elastic structures. The different problems are obtained varying the structure of the matrices belonging to the feasible set. Higham has solved the orthogonal, the symmetric and the positive definite cases. Andersson and Elfving have studied the symmetric positive semidefinite case and the (symmetric) elementwise nonnegative case. In this contribution, we extend and develop these research, however, in a relatively simple way. Numerical difficulties are discussed and illustrated by examples.

• The Journal of Information Systems
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• v.24 no.4
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• pp.43-60
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• 2015

Purpose This study intended to introduce new approaches to identify the intellectual structure of supply chain management(SCM) researches, which combines author co-citation analysis(ACA) and social network analysis(SNA). Design/methodology/approach We searched RISS(www.riss.kr) and NDSL(www.ndsl.or.kr) database and collected 292 academic papers on supply chain management between 2001 and 2011. Among 9,637 references of these papers, we analyzed 1,848 references that were published by domestic authors. We produced a correlation matrix of 32 author co-citation matrix and conducted multi-variate statistical analysis such as factor analysis. We also performed social network analysis to identify the main researchers in SCM. Findings We found four main sub-areas of supply chain management research: SCM adoption factors, logistics, SCM performance, and SCM structure. We could present the authors who played important roles within the network by using SNA indicators. The finding of this research also suggests more collaborations among domestic researchers are required to overcome the low co-citation rates among domestic authors.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.2
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• pp.593-606
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• 2017

This study aims to provide preliminary data capable of suggesting directivity of an initiating start by understanding consumer awareness through analysis of SNS social big data on marine sports. This study selected windsurfing, yacht, jet ski, scuba diving and sea fishing as research subjects, and produced following results by setting period of total 1 month from January 22 through February 22, 2017 on the SNS (twitter, blog) through the Social MatrixTM service of Daumsoft Co., Ltd., and analyzing frequency of mention, associated words etc. First, sports that was mentioned the most out of marine sports was yacht, which was 3,273 cases on twitter and 2,199 on blog respectively. Second, the word which was shown the most associated with marine sports was the attribute showing unique characteristic of marine sports, which was 6,261 cases in total.

• Annals of Geriatric Medicine and Research
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• v.20 no.3
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• pp.149-159
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• 2016

Background: Since the mid-20th century, the ways in which social networks and older adults' health are related have been widely studied. However, few studies investigate the relationship between self-rated health and position in a complete social network of one entire Korean rural village. This study highlights use of a complete network in health studies. Methods: Using the Korean Social Life and Health Project, the population-based data of adults aged 60 or older and their spouses in one myeon in Ganghwa island (Ganghwa-gun, Incheon, Korea), Incheon, Korea (with a 95% response rate), this study built a $1,012{\times}1,012$ complete social network matrix of the village. The data were collected from 2011 to 2012, and 731 older adults were analyzed. The ordered logistic models to predict self-rated health allowed us to examine social factors from socio-demographic to individual community activities, ego-centered network characteristics, and positions in a complete network. Results: From the network data, 5 network components were identified. Even after controlling for all other factors, if a respondent belonged to a segregated component, the probability that he or she reported good health dropped substantially. Additionally, high in-degree centrality was connected to greater self-rated health. Conclusion: This finding highlights the importance of social position not only from the respondents' point of view but also from the entire village's perspective. Even if a respondent maintained a large social network, when all of those social ties belonged to a segregated group in the village, the respondent's health suffered from this segregation.