• The Research Journal of the Costume Culture
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• v.22 no.5
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• pp.760-775
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• 2014

In the era of 2.0 web, blog has become the media that men can express themselves with fashion more actively and independently, as paying much attention to their personal appearance and cultivating an upscale lifestyle. They often create their fashion images in the virtual space where enables a free and creative operations of self-expression. The study aims to identify the types of men's ego-images represented on the personal fashion blogs based on the framework of analysis from the previous research (Suh, 2014), to build the base data for analyzing men's fashion style in $21^{st}$21st century that reflects changes in men's sexual images, and to verify the framework as comparing with the previous case study about the women blogs (Suh, 2014). The case studies conducted 5 men's personal blogs such as bryanboy, iamgala, little fashionisto, katelovesme, and stylentonic. The study results almost same types of women's ego-images as following. The imaginary ego-image is classified as narcissism, regression, identification, and virtuality, the social ego-image as symbolism of roles and others'desire, the real ego as primary instinct, practical reality, object a, jouissance and sexual perversion. The personal style of men shown on the fashion blogs appears as a significant factor to analyze male customers in the growing men's beauty and fashion market.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.113-125
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• 2011

As information resources have become more various and the number of the resources has increased, knowledge customization on the social web has been becoming more difficult. To reduce the burden, we offer a framework for context-based similarity calculation for knowledge customization using ontology on the CBR. Thereby, we newly developed context- and intensity-based similarity calculation methods which are applied to extraction of the most similar case considered semantic similarity and syntactic, and effective creation of the user-tailored knowledge using the selected case. The process is comprised of conversion of unstructured web information into cases, extraction of an appropriate case according to the user requirements, and customization of the knowledge using the selected case. In the experimental section, the effectiveness of the developed similarity methods are compared with other edge-counting similarity methods using two classes which are compared with each other. It shows that our framework leads higher similarity values for conceptually close classes compared with other methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.99-106
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• 2017

With the increased interest in the quality of life of modern people, the implementation of the five-day working week, the increase in traffic convenience, and the economic and social development, domestic and international travel is becoming commonplace. Furthermore, in the past, there were many cases of purchasing packaged goods of specialized travel agencies. However, as the development of the Internet improved the accessibility of information about the travel area, the tourist is changing the trend to plan the trip such as the choice of the destination. Web services have been introduced to recommend travel destinations and travel routes according to these needs of the customers. Therefore, after reviewing some of the most popular web services today, such as Stubby planner (http://www.stubbyplanner.com) and Earthtory (http://www.earthtory.com), they were supposed to be based on traditional Traveling Salesman Problems (TSPs), and the travel routes recommended by them included some practical limitations. That is, they were not considered important issues in the actual journey, such as the use of various transportation, travel expenses, the number of days, and lodging. Moreover, although to recommend travel destinations, there have been various studies such as using IoT (Internet of Things) technology and the analysis of cyberspatial Big Data on the web and SNS (Social Networking Service), there is little research to support travel routes considering the practical constraints. Therefore, this study proposes a new mathematical model for applying to travel route recommendation service, and it is verified by numerical experiments on travel to Jeju Island and trip to Europe including Germany, France and Czech Republic. It also expects to be able to provide more useful information to tourists in their travel plans through linkage with the services for recommending tourist attractions built in the Internet environment.

• Journal of Information Technology Services
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• v.22 no.1
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• pp.75-93
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• 2023

The game industry has grown not only by combining them with various technologies also introducing new types of business models such as P2P, F2P, and P2W. Furthermore, games which implemented X2E model with blockchain technology are recently in the spotlight of the public attention. As domestic game companies have also prospect the blockchain games feasible, they are seeking ways to expand their global market share by strengthening the X2E model. Hence, by carrying this new business model out, it is expected to diversify their global revenue stream, which was previously confined to Asia region. This study analyzed the case of companies that have implemented the P2E and C2E models in order to suggest the direction of development for the game platform in Web 3.0 era. The cases of P2E game platform, which constitute of Axie Infinity and Mir 4, encompass the compensation structure, the stabilization mechanism of the in-game token economy, and future strategies regarding blockchain gaming. Likewise, the platform structure, business model, and future growth potential was discussed in terms of C2E scheme, focusing on the ZEPETO and Roblox cases. Based on the above case analysis, this study attempted to provide information on the current limitations and development directions of the P2E and C2E platforms. The current limitations in legal and industrial aspects should be addressed to facilitate the blooming of blockchain and P2E game industry. In addition, the necessity of not only social support also improvement on the technology and social stigma of full-time creators is ought to be emphasized in an effort to encourage the development of C2E platforms.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.170-174
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• 2017

Nowadays, people share and discuss scientific papers on social media such as the Web 2.0, big data, online forums, blogs, Twitter, Facebook and scholar community, etc. In addition to a variety of metrics such as numbers of citation, download, recommendation, etc., paper review text is also one of the effective resources for the study of scientific impact. The social media tools improve the research process: recording a series online scholarly behaviors. This paper aims to research the huge amount of paper reviews which have generated in the social media platforms to explore the implicit information about research papers. We implemented and shown the result of text mining on review texts using R language. And we found that Zika virus was the research hotspot and association research methods were widely used in 2016. We also mined the news review about one paper and derived the public opinion.

• International Journal of Knowledge Content Development & Technology
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• v.6 no.1
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• pp.5-39
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• 2016

Social networking applications (SNAs) are among the fastest growing web applications of recent years. In this paper, we propose a causal model to assess the success of SNAs, grounded on DeLone and McLean's updated information systems (IS) success model. In addition to their original three dimensions of quality, i.e., system quality, information quality and service quality, we propose that a fourth dimension - networking quality - contributes to SNA success. We empirically examined the proposed research model with a survey of 168 Facebook and 149 Twitter users. The data validates the significant role of networking quality in determining the focal SNA's success. The theoretical and practical implications are discussed.

• International Journal of Human Ecology
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• v.10 no.2
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• pp.71-78
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• 2009

Efforts were made to improve the efficiency of college education through the use of information technology. This paper investigates student perceptions of a lecture club provided on a social networking website. For the empirical study, the instructor ran a lecture club for two consecutive semesters on Cyworld (www.cyworld.co.kr), a popular website among Korean youth. The research subjects were students enrolled in a Popular Culture & Fashion class. A questionnaire was distributed on the last day of the lectures. After excluding students with perfunctory responses and those who did not sign up for the community website, a total number of 297 questionnaires were used for analysis. Descriptive statistics, Pearson correlation analysis, one-way ANOVA analysis, Duncan test, and t-test were carried out, with the SPSS for Windows 12.0 being used for statistical analysis. The findings show that most students subscribed to the website and responded with a favorable attitude that the lecture club was helpful.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.97-104
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• 2022

In recent years, data privacy has become increasingly important. The goal of network cryptography is to protect data while it is being transmitted over the internet or a network. Social media and smartphone apps collect a lot of personal data which if exposed, might be damaging to privacy. As a result, sensitive data is exposed and data is shared without the data owner's consent. Personal Information is one of the concerns in data privacy. Protecting user data and sensitive information is the first step to keeping user data private. Many applications user data can be found on other websites. In this paper, we discuss the issue of privacy and suggest a mechanism for keeping user data hidden in other applications.