• International journal of advanced smart convergence
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• v.12 no.4
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• pp.202-207
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• 2023

In this paper, we discuss the design and implementation of a recommendation platform actually built in the field. We survey deep learning-based recommendation models that are effective in reflecting individual user characteristics. The recently proposed RNN-based sequential recommendation models reflect individual user characteristics well. The recommendation platform we proposed has an architecture that can collect, store, and process big data from a company's commercial services. Our recommendation platform provides service providers with intuitive tools to evaluate and apply timely optimized recommendation models. In the model evaluation we performed, RNN-based sequential recommendation models showed high scores.

• Journal of Distribution Science
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• v.22 no.9
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• pp.39-48
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• 2024

Purpose: The purpose of this study is to collect and analyze a large amount of data from online ticket distribution platforms that offer multiple airlines and different routes so that they can improve their ticket distribution marketing strategies and provide services that are more suitable for consumer's needs. The results of this study will help airlines improve the quality of their online platform services to provide more benefits and convenience by providing access to multiple airlines and routes around the world on one platform. Research design, data and methodology: For the study, 200 people completed the survey between May 1 and June 15, 2024, of which 191 copies were used in the study. Results: The hypothesis testing results of this study showed that among the components of the recommendation algorithm, decision comport, novelty, and evoked interest recurrence had a positive effect on perceived recommendation quality, but curiosity did not have a positive effect on recommendation quality. The perceived recommendation quality of the online platform positively influenced recommendation satisfaction, and the higher the perceived recommendation quality, the higher the intention to continue the relationship. Finally, higher recommendation satisfaction was associated with higher relationship continuation intention. Conclusion: it's important to continue researching online ticketing platforms. Online platforms will also need to be systems that use technology and data analytics to provide a better user experience and more benefits.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.360-374
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• 2024

A personalized course recommendation algorithm based on deep learning in an online education cloud platform is proposed to address the challenges associated with effective information extraction and insufficient feature extraction. First, the user potential preferences are obtained through the course summary, course review information, user course history, and other data. Second, by embedding, the word vector is turned into a low-dimensional and dense real-valued vector, which is then fed into the compressed interaction network-deep neural network model. Finally, considering that learners and different interactive courses play different roles in the final recommendation and prediction results, an attention mechanism is introduced. The accuracy, recall rate, and F1 value of the proposed method are 0.851, 0.856, and 0.853, respectively, when the length of the recommendation list K is 35. Consequently, the proposed strategy outperforms the comparison model in terms of recommending customized course resources.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1075-1087
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• 2020

Aviation ICT technology is a convergence technology between aviation and electronics, and has a wide variety of applications, including navigation and education. Among them, in the field of aerial pilot training, there are many problems such as the possibility of accidents during training and the lack of coping skills for various situations. This raises the need for a simulated pilot training system similar to actual training. In this paper, pilot training data were collected in pilot training system using VR/AR to increase immersion in flight training, and Customized Pilot Training Platform with Collaborative Deep Learning in VR/AR Environment that can recommend effective training courses to pilots is proposed. To verify the accuracy of the recommendation, the performance of the proposed collaborative deep learning algorithm with the existing recommendation algorithm was evaluated, and the flight test score was measured based on the pilot's training data base, and the deviations of each result were compared. The proposed service platform can expect more reliable recommendation results than previous studies, and the user survey for verification showed high satisfaction.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.582-587
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• 2021

Book recommender system, which suggests book to users according to their book taste and preference effectively improves users' book-reading experience and exposes them to variety of books. Insufficient dataset of book rating records by users degrades the quality of recommendation. In this study, we suggest a book recommendation system that makes use of user's book ratings collaboratively with user's movie ratings where more abundant datasets are available. Through comprehensive experiment, we prove that our methods improve the recommendation quality and effectively recommends more diverse kind of books. In addition, this will be the first attempt for book recommendation system to utilize movie rating data, which is from the media-platform other than books.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.131-143
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• 2021

In this study, the factors influencing the satisfaction of the online platform musical viewing method were investigated, and the effect of the satisfaction factors on the recommendation intention and rewatching intention of the online platform viewing method for musicals was investigated. In addition, the effect of the survey subjects' degree of involvement to musicals between the satisfaction of the online platform-based musical viewing method and recommendation intention, and rewatching intention was investigated. Satisfaction factors of online platform musicals, which are independent variables, were classified into image quality, convenience, economy, and interactivity, and dependent variables were classified into recommendation intention and rewatching intention of online platform musicals, and moderator variable was set to degree of involvement to musicals, and a total of 20 hypotheses were established. An online survey was conducted on 1,454 audiences who had experience watching musicals through the online platform from August 28 to September 7, 2021, and a total of 1,418 answers were used as valid samples. As a result of the analysis, the factors that make up the satisfaction of online platform musicals appeared in the order of convenience, video quality, economics, and interactivity. It was found that the satisfaction level of watching online platform musicals had a positive effect on the intention to recommend and rewatching online platform musicals in the path of all satisfaction factors. It was found that the moderating effect of the audience's involvement in musicals between online platform musical viewing satisfaction and recommendation intention and rewatching intention had a significant effect only between image quality and recommendation intention. It shows that audiences with high involvement in musicals have intention to recommend only when they are satisfied with the video quality of online platform musicals. Particularly important point is that the convenience factor was found to have the greatest influence on the satisfaction of online platform musical viewing method, but the image quality factor was found to have the greatest influence on the recommendation intention and rewatching intention of online platform musicals.

• Knowledge Management Research
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• v.21 no.1
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• pp.175-195
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• 2020

The development of platform service based on the information and communication technology has revolutionized patterns of commercial transactions, driving the growth of global economy. Furthermore, the radical advancement of artificial intelligence(AI) presents the huge potential to innovate almost all the industrial and economic activities. Given these technological developments, the goal of this paper is to investigate AI's impact on the platform service innovation as well as its influence on the business performance. For the goal, this paper presents the review of the types of service innovation, the nature of platform services, and technological characteristics of leading AI technologies, such as chatbot and recommendation system. As an empirical study, this paper performs a multiple case study of Kakao Group which is the leading mobile platform service with the most advanced AI in Korea. To understand the role and effect of AI on Kakao platform service, this study investigated three cases, including chatbot agent of Kakao Bank, Smart Call service of Kakao Taxi, and music recommendation system of Kakao Mellon. The analysis results of the case study show that AI initiated innovations in platform service concepts, service delivery, and customer interface, all of which lead to a significant decrease in the transaction costs and the personalization of services. Finally, for the successful development of AI, this research emphasizes the significance of the accumulation of customer and operational data, the AI human capital, and the design of R&D organization.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.329-334
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• 2018

According to the textbook utilization survey, university students buy 6.4 books and spend 94,000 won per semester. However, nearly half of books are left unused. Therefore many students buy used textbooks instead of buying new books at a fixed price. The existing used textbook trading platforms support basic functions, but don't support textbook recommendation function and reference book recommendation function. In this paper, we developed a used textbook trading platform BookCue that provides textbook recommendation function, reference book recommendation function, and consignment trading function reflecting the regional characteristics. It is expected that will contribute to reduce university students' burden that buying textbook by forming textbooks trading environment and preserve environment. In the near future our platform will need to expand to a platform that deals with a variety of goods, as well as used textbooks in the region.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.164-175
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• 2022

The OTT (Over The Top) platform promotes itself as a distinctive competitive advantage in that it allows users to stay on the platform longer and visit more often through a Personalized Recommendation Service. In this study, the characteristics of the Personalized Recommendation Service are divided into three categories: recommendation accuracy, recommendation diversity, and recommendation novelty. Then proposed a research model which affects the usefulness of users to recognize recommendation services by each characteristics and leads to satisfaction of expectations. The result of conducting an online survey of 300 people in their 20s and 30s who subscribe Netflix shows that the perceived usefulness increased when the accuracy, variety, and novelty of Netflix's Recommendation Service were high. It was also confirmed that high perceived usefulness leads to satisfaction of expectations before and after Netflix use. The derived research results can confirm the importance of evaluating the personalized recommendation service in terms of user experience and provide implications for ways to improve the quality of recommendation services.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.523-528
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• 2021

Current YouTube recommends similar contents to users based on the contents they actually consumed. Due to the feature of these algorithms, users are well recommended for contents in similar fields, but it is difficult to be recommended contents in fields that have never been consumed. There is a limit to being widely recommended for videos. I want to solve this problem by utilizing crowd sourcing. I propose a platform that can be recommended for various channels, through direct participation of the public people using youtube. Users can be recommended a variety of channels, communicate with people in the channel discussion room, and at the same time generate revenue by recommending channels. I hope that this platform can be used in various crowd sourcing-based recommendation platforms.