• 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.

• Cartoon and Animation Studies
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• s.35
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• pp.291-312
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• 2014

This study is based on the theory of R.Caillois about element of play which is motivated to infant for studying Hangul. The ultimate goal of play has to be accompanied by pleasure. And learning means permanent changes from experiences for the individual's. Play and learning, these two elements are united to the genre of serious game since the GBL (game based learning) was lead. Most importantly, in order to achieve their own Hangul learning is the fun. Coupled with fun and learning has an important issue for flow because concentration is low in infants than adults. In this case study is to know about fun factor has been applied effectively to Hangul serious mobile game. 20 Infant Hangul mobile serious games of Google Android mobile game section were selected as a case study based on more than 10,000 downloads and user's review rate by April 22, 2014. After that is currently available on the market can play a variety of cases of infant learning Hangul from previous research of R.Caillois offers four categories of play. R.Caillois of Agon, Mimicry, Alea, Ilinx have unique characteristics in comparison with its functional characteristics Hangul four are present any role in Hangul serious mobile games. As a result of the cases selected and the rules of the game will include a maximum of two of the most common types of Agon. Each attribute of the play, rather than one single factor is applied to four kinds of game play performance when properties are distributed to experience together gave the best flow. As a result of this study will be a based research for infants Hangul serious mobile game reflects the properties of the elements of a fun game that you want to combine learning.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.149-158
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• 2002

For the purpose of preciously describing real time traffic pattern in urban road network, dynamic network loading(DNL) models able to simulate traffic behavior are required. A number of different methods are available, including macroscopic, microscopic dynamic network models, as well as analytical model. Equivalency minimization problem and Variation inequality problem are the analytical models, which include explicit mathematical travel cost function for describing traffic behaviors on the network. While microscopic simulation models move vehicles according to behavioral car-following and cell-transmission. However, DNL models embedding such travel time function have some limitations ; analytical model has lacking of describing traffic characteristics such as relations between flow and speed, between speed and density Microscopic simulation models are the most detailed and realistic, but they are difficult to calibrate and may not be the most practical tools for large-scale networks. To cope with such problems, this paper develops a new DNL model appropriate for dynamic traffic assignment(DTA), The model is combined with vertical queue model representing vehicles as vertical queues at the end of links. In order to compare and to assess the model, we use a contrived example network. From the numerical results, we found that the DNL model presented in the paper were able to describe traffic characteristics with reasonable amount of computing time. The model also showed good relationship between travel time and traffic flow and expressed the feature of backward turn at near capacity.

• Journal of The Korean Association For Science Education
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• v.19 no.3
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• pp.428-440
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• 1999

The colorful overhead projector films, named as Bio-cosmos II, including photographs, pictures, concept maps, and diagrams, were developed and manufactured as audio-visual teaching aids and teaching resources for students' biology learning in high school, and the CD-ROM and web sites for their application to the school were also constructed. The content of the films was organized based upon the analysis of seven different biology textbooks approved by the Ministry of Education. The films were designated based on various instructional strategies and manufactured using multimedia with various educational softwares. The CD-ROM was composed of the scenes as logo, initial main, chapters list, contents, and quit. Initial main scene indicated various chapters according to the texts of biology areas in General Science, Biology I, and II. Each chapters linked with the scenes for detailed concept maps, the downstream real subjects, and contents. The subject screens were composed of various types of summarized diagrams including lesson contents, figures, pictures, photographs, and their explanation, experimental procedures and results, tables for summarized contents, and additional animation with video captures, explanations, glossary, etc. Most files were manufactured in software Adobe Photoshop by scanning the pictures, figures and photographs, and then the explanation, modification, storing with PICT or PSD files, and transformation with JPG files, were processed in the aspect of high quality in terms of instructional strategies and graphic skills on gracefulness, clearness, colorfulness, brightness, and distinctness. A 14 films for biology areas in General Science, 80 for Biology I, and 142 for Biology II were manufactured and loaded to the CD-ROM and web site, and the files had been attempted to opened with an internet home-page of http://gic.kyungpook.ac.kr/biocosmos.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.

• Journal of Life Science
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• v.17 no.3 s.83
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• pp.368-374
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• 2007

The purpose of this study was to investigate the effects of different type of triglycerides (MCT & LCT) on weight, survival time, energy substrate (FFA, TG, pyruvate, lactate), insulin and lipase in the trained rats. Fifty-four Sprague-Dawley rats were divided into 3 groups: control group (CG, n=18), MCT supplement group (MG, n=18), and LCT supplement group (LG, n=18). They also were divided into 3 periods: trained resting (R, n=6) and trained & exercise load (E, n=6), and survival time test was performed to know the supplemented effects. Body weight of all animals was checked every week, MCT group and LCT group received supplementary MCT and LCT orally and preliminary swimming training for 6 days before the start of main experiment. All animals received 15-minute swimming training 5 times during first week of experiment, and swimming training time was increased 15 minutes every week until it reached 90 minutes at last 9th week. After last swimming training, animals were fasted for 12 hours and blood samples were taken from abdominal aorta in the Department of Animal Medicine at the D university Animal Center. Among the CGE, MGE, and LGE groups, the MGE had the greatest increase in physical performance time. In the FFA levels, there was significant differences(p<.05) in CG, MG and LG groups, and also there was major difference of FFA levels in the MG and LG. In the lipase levels, there was signifi.ant differences (p<.05) in CG, MG and LG groups. MG was the greatest than the other groups. In the insulin hormone levels, there was the great differences (p<.05) in LG compare to CG groups, whereas there was no significant difference in the CG and MG. In conclusion, these results suggest that regular prolonged physical training with MCT supplementation, improves exercise performance time through the increase of energy substrate utilization, lipase activity and FFA levels, irrespective of insulin hormone responses.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.