• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.1-13
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• 2021

With the global pandemic of COVID-19, it is pointed out that exposure to false information to the public could cause serious problems. However, in pandemic situations, there is also an positive effect for the public to share private-led information rather than centralized unilateral delivery of information. This study analyzes the role of private-led information provision in infectious disease situations. To this end, topic modeling and sentiment analysis is carried out on online reviews of all COVID-19-related applications in Google Playstore provided by the Korean government and the private. The results showed that the user's evaluation of private apps, which were used from the early stage of COVID-19, was much higher than the apps provided by the government. In particular, users responded more positively to private apps than government apps in all aspects such as reliability of information, risk avoidance, timeliness, usefulness, and stability. Based on these results, a post-monitoring system is recommended rather than a pre-block of all private apps.

• Journal of Creative Information Culture
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• v.5 no.3
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• pp.329-343
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• 2019

This study designed, developed and evaluated an application to improve the social skills of students with Autism Spectrum Disorders(ASD) based on the stage of the ADDIE model. The design stage aimed to factor in the characteristics and needs of students with ASD, and created guide maps and story boards accordingly. In the development stage, the application was made through a total of fourteen processes and four official tests. In the evaluation stage, heuristic usability evaluation was conducted on 10 teachers based on the their experience of using the application in the implementation stage. The results of the evaluation were all close to 'very good' in the areas of educational value, content implementation technology, content information and convenience. Finally, the application was fully developed after reflecting the corrections in accordance with the result of the usability evaluation. It is expected that this study have a role in invigorating the development of special educational software content.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• Cartoon and Animation Studies
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• s.40
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• pp.423-442
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• 2015

In a digital environment that is keep in change content from smart phones to tablet PC are a social network service is holding deep place in our lives. Social networking applications has building a network and communication with others than other application, it means that Social networking applications are sharing not only personal purpose in that trend of variety and competition of these social networks can be expected to trend and be developed thru analysis of user certification. this study of social network service application is proposed to developing of application thru analyze the two-effective application which is high ranked in google store. the theoretical foundation was set based on the seven elements of the social network service of the information structures designed by Jean Smith. This study proceeded analysis is for the functional elements of Facebook and Instagram, and the advantages and disadvantages through survey research. As a result of the empirical analysis to user of Instagram and face book of communication, identity, satisfaction for the group are equally. Instagram is about the presence, reputation, and Facebook has had a high level of satisfaction for each sharing and relationship. Facebook got high satisfaction from sharing features, but user feel of discomfort in the randomly showing advertising content. Instagram is not showing off advertise on common page of content, it is good point to be complementary to facebook. And, Instagram hashtag is good for convenience, but satisfaction is high with Facebook. in order to increase the satisfaction of Instagram, it is necessary to consider the main advantage of the communication and the functional aspects of the share from facebook.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.163-171
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• 2020

Currently, most of the mobile game market releases games through Google play and App Store, which have a high share. Because it uses a third-party platform, only the payment API system provided must be used, and third-party platform pays the game company after excluding certain fees. Because game companies do not know whether or not to refund items and cannot get back items through third party transactions, users and professional websites are continuously appearing that exploit refunds. In this thesis, after analyzing problems of existing payment method and presenting a payment model using blockchain smart contract, we analyzed differences from existing model in terms of transparency, decentralization(fee), efficiency, and as a result, payment model using smart contract has low commission through P2P transaction without third parties and transparent transaction record, preventing item forgery and refund. Later, the proposed payment model would lead to the culling of companies acting on behalf of refunds for words that deviate from moral ethics such as "Refund OK even with items" and resolve the problem of unreasonable fees that arise through third-party platforms.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.69-90
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• 2023

The purpose of this study is to evaluate the service attributes and consumer reactions of telemedicine apps in South Korea and visualize their differentiation by constructing positioning maps. We crawled 23,219 user reviews of 6 major telemedicine apps in Korea from the Google Play store. Topics were derived by BERTopic modeling, and sentiment scores for each topic were calculated through KoBERT sentiment analysis. As a result, five service characteristics in the application attribute category and three in the medical service category were derived. Based on this, a two-dimensional positioning map was constructed through principal component analysis. This study proposes an objective service evaluation method based on text mining, which has implications. In sum, this study combines empirical statistical methods and text mining techniques based on user review texts of telemedicine apps. It presents a system of service attribute elicitation, sentiment analysis, and product positioning. This can serve as an effective way to objectively diagnose the service quality and consumer responses of telemedicine applications.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.473-483
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• 2020

This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.555-564
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• 2013

Innovative evolution in mobile devices along with recent spread of Tablet PCs and Smart Phones makes a new change not only in individual life but also in enterprise applications. Especially, in the case of medium-large mobile applications for large enterprises which generally takes more than 3 months of development periods, importance and complexity increase significantly. Generally Agile-methodology is used for a development process for the medium-large scale mobile applications, but some issues arise such as high dependency on skilled developers and lack of detail development directives. In this paper, S-MADP (Smart Mobile Application Development Process) is proposed to mitigate these issues. S-MADP is a service oriented development process extending a object-oriented development process, for medium-large scale mobile applications. S-MADP provides detail development directives for each activities during the entire process for defining services as server-based or client-based and providing the way of reuse of services. Also, in order to support various user interfaces, S-MADP provides detail UI development directives. To evaluate the performance of S-MADP, three mobile application development projects were conducted and the results were analyzed. The projects are 'TBS(TB Mobile Service) 3.0' in TB company, mobile app-store in TS company, and mobile groupware in TG group. As a result of the projects, S-MADP accounts for more detailed design information about 'Minimizing the use of resources', 'Service-based designing' and 'User interface optimized for mobile devices' which are needed to be largely considered for mobile application development environment when we compare with existing Agile-methodology. Therefore, it improves the usability, maintainability, efficiency of developed mobile applications. Through field tests, it is observed that S-MADP outperforms about 25% than a Agile-methodology in the aspect of the required man-month for developing a medium-large mobile application.

• Journal of Legislation Research
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• no.41
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• pp.401-434
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• 2011

The discussion on the adoption of the horizontal regulatory framework is underway to overcome the problems regarding the vertical regulatory framework resulting from a convergence of broadcasting and telecommunications services. Recently, however, the horizontal regulatory framework shows its limitation to regulate the ecosystem established mainly by Google and Apple. The existing horizontal regulatory framework does not fully reflect the characteristics of the two-sided market and the change in the competition structure in the broadcasting and telecommunications sector. What is important to note is that if the existing horizontal regulatory framework is simply applied to the ecosystem, a regulatory imbalance can be caused among ecosystems. The existing horizontal regulatory framework, which is subject to a value chain structure, categorizes business entities into either contents layer or transmission layer and applies the same regulation to all business entities in the same category. However, in the ecosystem, a keystone-player can be categorized into different layers depending on its strategy. Therefore, if the existing horizontal regulatory framework is applied as it is, the regulatory imbalance between keystone-players located in less regulated areas and keystone-players located in more regulated areas occurs resulting in a distortion of competition. There are two possible ways to establish a new regulatory framework to prevent the distortion of competition likely to be caused by the adoption of a horizontal regulatory system. First, a new ecosystem regulatory framework different from the existing one can be established. Second, the horizontal regulatory framework can be modified to reflect the ecosystem elements. The first approach is hard to adopt given the current situation as the approach requires the analysis of all broadcasting and telecommunications ecosystems including mobile and wired services; currently research and study on the competition conditions in the ecosystems is not enough. Therefore, this paper supports the second approach proposing a modified horizontal regulatory framework through the improvement of institutions and remedies suitable for accommodating the ecosystem elements. This paper intends to propose a way to regulate broadcasting and telecommunications ecosystems taking into consideration the ecosystem elements on top of the Telecommunications Business Act, Broadcasting Act, IPTV Act, the competition condition evaluation system of the Basic Act on Broadcasting and Telecommunications Development, and regulation on common carriers under the Telecommunications Business Act.