• Smart Media Journal
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• v.12 no.6
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• pp.27-34
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• 2023

As Netflix usage has increased due to the COVID-19 pandemic, users' experiences with the service have also increased. Therefore, this study aims to conduct topic modeling analysis based on Netflix review data to explore the changes in Netflix user experience and service before and after the COVID-19 pandemic. We collected Netflix app review data from the Google Play Store using the Google Play Scraper library, and used topic modeling to examine keyword differences between app reviews before and after the pandemic. The analysis revealed four main topics: Netflix app features, Netflix content, Netflix service usage, and Netflix overall reviews. After the pandemic, when user experience increased, users tended to use more diverse and detailed keywords in their reviews. By using Netflix review data to analyze users' opinions, this study shows the changes in user experience of Netflix services before and after the pandemic, which can be used as a guide to strengthen competitiveness in the competitive OTT market.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.437-447
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• 2018

Virtual Reality (VR) is becoming a new standard in the game industry. PokeMon GO is a representative example of VR technology. The day after the launch of PokeMon Go in the U.S, It has achieved the highest number of iOS App Store downloads. This is an example of the power of VR. VR comprises gyroscopes, acceleration, tactile sensors, and so on. This allow users could be immersed in the game. As new technologies emerge, new and different threats are created. So we need to research the security of VR technology and game system. In this paper, we conduct a threat analysis for information assurance of VR device (Oculus Rift) and game system (Quake). We systematically analyze the threats (STRIDE, attack library, and attack tree). We propose security measures through DREAD. In addition, we use Visual Code Grepper (VCG) tool to find out logic errors and vulnerable functions in source code, and propose a method to solve them.

• Journal of Korea Game Society
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• v.11 no.6
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• pp.139-148
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• 2011

Various types of games have been published in a smart phone game market. However, there are little serious simulation games among them. In this study, parking simulation game has been developed and published on the Apple app store and Android market, to analyze the usability and prospects of the smart phone based serious simulation game. Developed game was designed to give users realistic simulation experience with immersion in the smart phone environment. In case of Apple app store, more than 300 thousands download have been recorded. This means serious simulation games have much more potential for commercial value, even though there are constrains with small display and user interface.

• Journal of Digital Convergence
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• v.9 no.4
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• pp.169-179
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• 2011

The purpose of this study are as follows. First, to analyze digital convergence level of convergence generation to demographic variables. Second, the convergence level to digital device and using frequency to digital service. Third, the convergence level to digital service and using frequency to digital service. The research methods FGI, the interview with IT expert group and survey. The results of research are as follows. First, 30 aging, expert group, higher education group over graduate school are actively using and participated. Second, high level of convergence device are smart-phone, tablet PC, net-book are in order. high level of convergence service are SNS service, twitter, uee, portal messenger and app store, e-Book, web hard are in order. Third, The convergence generation enjoying app-store of smartphone, wireless game and more participating facebook/cyworld twitter, Portal, internet community.

• The Research Journal of the Costume Culture
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• v.30 no.5
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• pp.656-674
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• 2022

The purpose of this study is to reveal and compare the differences in the types and characteristics of purchase channel journeys of MZ generation consumers. In this study a survey was conducted on the purchase channel journey of 20 women in the MZ generation using the ethnographic method of in-depth interviews and observations. As a result, three purchase channel journeys were identified: mobile, multi-channel, and offline. These were variously subdivided according to the characteristics of the MZ generations. Gen Z's journey was categorized into types: fashion platform app, Youtube, multi-channel supplement, multi-channel non-planned store visit, offline loyalty store, and impulsive offline store. Gen M's journey was categorized as: an online community bond, portal site, online loyalty store, multi-channel brand involvement, multi-channel efficiency, a multi-channel conversion, offline efficiency and offline task. The difference in mobile journey between generations was found in the time and length of the purchase. Gen M recognized both online and offline search processes to be tiring, while Gen Z enjoyed the search process using the online path. In the offline journey Gen Z began with their own intention to purchase, while Gen M sometimes recognized that purchasing fashion products necessary for work was a cumbersome task.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.177-182
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• 2023

When determining a meeting place, it is always a cumbersome problem to select an appropriate store with a short travel time for all participants. In this paper, to solve this problem, we propose an algorithm that recommends the best place and store based on the subway station and develop the system. This system provides a web-based store information registration function that allows self-employed people to register and promote their store, and provides an app-based function to recommend a meeting place to participants. The proposed algorithm reduces the travel time of all participants based on the subway map and improves fairness by using the standard deviation of the required time. In addition, this system presents a new way for self-employed people who have recently relied only on publicity through delivery apps.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.948-956
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• 2016

Many franchise stores are in the surrounding. These stores offer many membership services. But, small traders do not provide much customer management service. For example, non-franchised cafes provide the services through paper coupons. However, most of paper coupons are available only in one store that issued the coupon. Besides, these coupons are in stamp format. Due to the absence of customers' management service, it is hard for small traders to attract customers compared to the franchise. Therefore, in this paper, We had implemented a mobile application, and applied to new customer management service in order to increase the price competitiveness of small traders. This service issues electronic coupon through a mobile app. customers can receive a relatively large amount of discount, and a deadline of coupons are short. When customers use coupons, new coupons are issued at the same time. This structure can be powerful means to lead customers' frequent visit. Small traders can gain a lot of regular customers by using this service.

• Journal of Fashion Business
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• v.22 no.6
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• pp.83-93
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• 2018

The purpose of this research is to investigate the characteristics of big data-based fashion shopping (BDFS) application, perceived usefulness, and expectation confirmation that influence the continuous usage intention of BDFS application users based on the expectation-confirmation model. A survey was conducted with female consumers in their 20s, who are living in Seoul and Incheon area and have used BDFS applications, A total of 182 responses were used for the data analysis. Five hypotheses were proposed, and regression analyses were conducted to test those hypotheses. The results indicated that the users' perceived usefulness increased with the increase of accuracy and personalization characteristics of the app and the expectation confirmation. The result suggested that it is essential to provide accurate information for users to feel useful and to develop the personalized offerings and services which can be the biggest strength of the big-data based mobile fashion store. It was also found that continuous usage intention increases with increased perceived usefulness and expectation confirmation. This result suggests that expectations can play a critical role in perceiving the usefulness of BDFS applications and the user's expectation confirmation also significantly affected the users' continuous usage intention.

• The Journal of Information Systems
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• v.29 no.4
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• pp.137-151
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• 2020

Purpose Domestic asset management fintech companies are expected to grow by leaps and bounds along with the implementation of the "Data bills." Contrary to the market fever, however, academic research is insufficient. Therefore, we want to analyze user reviews of asset management fintech companies that are expected to grow significantly in the future to derive strengths and complementary points of services that have been provided, and analyze key elements of asset management fintech companies. Design/methodology/approach To analyze large amounts of review text data, this study applied text mining techniques. Bank Salad and Toss, domestic asset management application services, were selected for the study. To get the data, app reviews were crawled in the online app store and preprocessed using natural language processing techniques. Topic Modeling and Aspect-Sentiment Analysis were used as analysis methods. Findings According to the analysis results, this study was able to derive the elements that asset management fintech companies should have. As a result of Topic Modeling, 7 topics were derived from Bank Salad and Toss respectively. As a result, topics related to function and usage and topics on stability and marketing were extracted. Sentiment Analysis showed that users responded positively to function-related topics, but negatively to usage-related topics and stability topics. Through this, we were able to extract the key elements needed for asset management fintech companies.