• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.4
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• pp.251-265
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• 2013

The emergence of social network services, is changing the foundation of human relationship formation and method of communication of individuals through sharing of free information. Social network service is a service to support or facilitate an on-line extension of off-line network among people by helping them to share personal profile. History of social network services very short. But users of the various layers is increasing rapidly and ripple effect social as a result is very large. The focus of existing research was mainly devoted to motivation of use and acceptance of social network services. Currently the use of SNS was maturing. Thus, in-depth research on the use pattern of SNS users is needed. The purpose of this study is that, for Facebook in social network services, to analyze the changes in the initial stage of use, medium-term, usage patterns at the current time. Results of the study by analyzing the characteristics of the change in the pattern of usage of user of Facebook, it can be used as basic materials for SNS researchers and service provider.

• Annual Conference of KIPS
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• 2022.05a
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• pp.385-387
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• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.

• The Journal of Korean Society for School & Community Health Education
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• v.9 no.2
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• pp.65-80
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• 2008

Objectives: To identify the relationship between the internet addition of elementary school students, and their health behavior and VDT syndrome, and thereby to detect the impact of internet addiction on the health behavior of elementary school students, to get the basic information necessary to develop a prevention program for internet addiction and to plan for VDT syndrome prevention program. Methods: We conducted this study during the period from jun 27, 2007 through July 8, 2007. The subjects of this study were 416 children whose grades were in 4th through 6th grades of elementary schools located in the city A in Kyung Pook Providence. Data were obtained from self-rating questionnaires. The questionnaires were composed of Korean Internet Addiction Self-examination on Internet Use Patterns (K-scale), Health Behavior Profile, VDT Syndrome, and general characteristics. We used t-test, AVOVA with Ducan method for Post-hoc comparison in means comparison between groups, $X^2$-test for frequency analysis and Pearson's correlation coefficient. We used SPSS/PC(12.0 ver) program and the LISREL 8.53 Win program for covariance structural analysis. Results: Major results were as follows. 1. The internet addiction propensity distribution based on the distribution of scores were investigated according to the self diagnosis scale on internet addiction. 6.5% of them were high risk user group, 14.4% were potential risk users and 79.1% of them were common user groups. 2. Internet addictions by sex, internet use duration, frequency (days/week), time(hours/day), purpose, position, brightness of internet, attitude of parents and frequency of conversation of family members were statistically significant(p<0.01). 3. There was a statistically significant difference in VDT syndrome according to internet addiction groups(p<0.001) besides ocular symptoms, dry mouth and GI troubles. 4. The health behavior score was the lowest in high risk user group(p<0.001). There were significant differences between internet addiction groups in personal hygiene and habits of daily living(p<.002), the prevention of accidents(p<.002), the practice concerned with the prevention of infectious disease(p=.002), and mental health(p<.001). 5. There was also a significant negative correlation between internal addiction and health profile(r=-0.365, p<0.01) and a significant positive correlation between internal addiction and VDT syndrome(r=0.331, p<0.01). 6. As the result of structural model analysis, internet use time(/day), days of internet use(/1week), conversation frequency among family members, degree of brightness of internet use had significant direct effects on internet addiction. Conclusions: The results will help the development of an effective intervention program for the prevention and treatment of internal addiction by clarifying the effect of the internal addiction upon elementary school students' VDT syndrome and health behavior.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.73-85
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• 2014

This study analyzed the profile of a Facebook account to examine how personal information is revealed and what kinds of factors influence personal information revelation. Categories of user's profile on Facebook were analyzed and two dimensions were developed; the degree that how much personal information is revealed and the network limits that personal information is accessed. Main variables to determine personal information revelation are Facebook privacy concern and uses for social relationships along with gender, the duration of Facebook use, and average time of use. Data were collected from college students. Factor analysis produced two factors of Facebook privacy concern, Facebook privacy concern with users and Facebook privacy concern with the Facebook system. Regression analyses were performed to identify significant determinants of the degree of information revelation and the network limits of personal information. The results found out that the degree of personal information revelation is explained by gender, the duration of use, and use for social relationships while the network limit is explained by the duration of use and Facebook privacy concern with users. Worthy of notice is that use for social relationships and Facebook privacy concern with the Facebook system offset each other. The implications of the results are discussed. Additionally and finally the categories of profiles are graphically re-grouped to show how personal information revelation is associated with social relationship generation and maintenance.

• Journal of Advanced Navigation Technology
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• v.2 no.2
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• pp.132-142
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• 1998

In optical scanning holography, 3-D holographic information of an object is generated by 2-D active optical scanning. The optical scanning beam can be a time-dependent Gaussian apodized Fresnel zone plate. In this technique, the holographic information manifests itself as an electrical signal which can be sent to an electron-beam-addressed spatial light modulator for coherent image reconstruction. This technique can be applied to 3-D optical remote sensing especially for identifying flying objects. In this paper, we first briefly review optical scanning holography and analyze the resolution achievable with the system. We then present mathematical expression of real and virtual image which are responsible for holographic image reconstruction by using Gaussian beam profile.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1356-1363
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• 2004

We propose Tangible Media Control System (TMCS), which allows users to manipulate media contents with physical objects in an intuitive way. Currently, most people access digital media contents by exploiting GUI. However, It provides limited manipulations of the media contents. The proposed system, instead of mouse and keyboard, adopts two types of tangible objects, i.e RFID-enabled object and tracker-embedded object. The TMCS enables users to easily access and control digital media contents with the tangible objects. In addition, it supports an interactive media controller which users can synthesize media contents and generate new media contents according to users' taste. It also offers personalized contents, which is suitable for users' preferences, by exploiting context such as user's profile and situational information. Therefore. the proposed system can be applied to various interactive applications such as multimedia education, entertainment and multimedia editor.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.227-235
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• 2010

This paper proposes the recommendation system which is advanced using RFM method and Association Rules in e-Commerce. Using a implicit method which is not used user's profile for rating, it is necessary for user to keep the RFM score and Association Rules about users and items based on the whole purchased data in order to recommend the items. This proposing system is possible to advance recommendation system using RFM method and Association Rules for cross-selling, and also this system can avoid the duplicated recommendation by the cross comparison with having recommended items before. And also, it's efficient for them to build the strategy for marketing and crm(customer relationship management). It can be improved and evaluated according to the criteria of logicality through the experiment with dataset collected in a cosmetic cyber shopping mall. Finally, it is able to realize the personalized recommendation system for one to one web marketing in e-Commerce.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.41-50
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• 2008

This paper proposes the recommendation system which is a new method using RFM method in mobile internet environment. Using a implict method which is not used user's profile for rating, is not used complicated query processing of the request and the response for rating, it is necessary for user to keep the RFM score about users and items based on the whole purchased data in order to recommend the items. As there are some problems which didn't exactly recommend the items with high purchasablity for new customer and new item that do not have the purchase history data. in existing recommendation systems, this proposing system is possible to solve existing problems, and also this system can avoid the duplicated recommendation by the cross comparison with the purchase history data. It can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic cyber shopping mall. Finally, it is able to realize the personalized recommendation system with high purchasablity for one to one web marketing through the mobile internet.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.953-964
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• 2006

Due to the usefulness of the collaborative filtering, it has been widely used in both the research and commercial field. However, there are still some challenges for it to be more efficient, especially the scalability problem, the sparsity problem and the cold start problem. In this paper. we address these problems and provide a novel distributed approach based on agents collaboration for the problems. We have tried to solve the scalability problem by making each agent save its users ratings and broadcast them to the users friends so that only friends ratings and his own ratings are kept in an agents local database. To reduce quality degradation of recommendation caused by the lack of rating data, we introduce a method using friends opinions instead of real rating data when they are not available. We also suggest a collaborative filtering algorithm based on user profile to provide new users with recommendation service. Experiments show that our suggested approach is helpful to the new user problem as well as is more scalable than traditional centralized CF filtering systems and alleviate the sparsity problem.