• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.109-110
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• 2017

현재 국내외에 거실을 잡기위한 노력들이 전방위적으로 이뤄지고 있다. 거실에서 쉽게 쇼핑을 하고, 커튼이나 조명을 켜고, 음악을 듣고, 피자를 시켜 먹으며, 외부 약속을 위해 택시를 부르는, 글로벌 기업들의 '스마트 홈'의 기능으로 인공지능과 음성인식을 통한 산물이 되었다. 또한 이러한 데이터를 중심으로 빅데이터의 보고가 되어간다. 때문에 구글, 아마존, MS, 삼성과 우리나라의 SKT와 LG 등의 기업들이 이러한 기술기반으로 접근하는 현황을 파악하고, 기술에 대한 적정성을 제안할 필요가 있다.

• Journal of Venture Innovation
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• v.4 no.1
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• pp.87-99
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• 2021

The importance of "personalization technology" has recently been highlighted due to the Covid-19 and the development of IT technology such as AI and big data, which is soon coming beyond personalization into the "super-personalization era." Therefore, in terms of the music streaming service market, it has formed a service supply trend in which individual tastes are respected and companies are seeking to establish a realistic analysis and development direction considering the external market environment. From this perspective, this paper sought to analyze the strengths and weaknesses of the Genie Music's and provide a direction for development based on Genie Music's customer interactive strategy. In particular, it was intended to analyze the advantages and disadvantages of customer interactive strategies with the 'live music service platform' that moves with customers and to provide directions for future corporate development. As an analysis method, we looked at strengths and weaknesses, opportunities and threat requirements based on SWOT analysis. Afterwards, the company attempted to present specific corporate development strategies through TOWS analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.603-605
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• 2022

추천시스템은 개개인의 성향에 따른 맞춤화 추천이 가능하기 때문에 음악, 영상, 뉴스 등 많은 분야에서 관심을 받고 있다. 일반적인 추천시스템 모델은 블랙박스 모델이기 때문에 추천 결과에 따른 원인 도출을 할 수 없다. 하지만 XAI 의 모델은 이러한 블랙박스 모델의 단점을 해결하고자 제안되었다. 그 중 KGAT 는 Attention Score 를 기반으로 추천 결과에 따른 원인을 알 수 있다. 이와 같은 AI, XAI 등의 딥 러닝 모델에서 각각의 활성화 함수는 상황에 따라 상이한 성능을 나타낸다. 이러한 이유로 인해 데이터에 맞는 활성화 함수를 적용해보는 다양한 시도가 필요하다. 따라서 본 논문은 XAI 추천시스템 모델인 KGAT 의 성능 개선을 위해 여러 활성화 함수를 적용해보고, 실험을 통해 수정한 모델의 성능이 개선됨을 보인다.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.57-62
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• 2022

This study was conducted with the purpose of listening to the explanations of artworks in the non-face-to-face class and confirming the learner's feelings as a result of the class. The proposed system listens to the explanation of the artwork, inputs the learner's emotions with a dedicated key, and expresses the result in music. To this end, the direction of the non-face-to-face art appreciation class model using the emotion key was set, and based on this, a system for non-face-to-face art appreciation was constructed. The learner will use the 'smart device using the emotion key' proposed in this study to listen to the explanation of the artwork and to input the emotion for the question presented. Through the proposed system, learners can express their emotional state in online art classes, and instructors receive the results of class participation and use them in various ways for educational analysis.

• Information Systems Review
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• v.22 no.2
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• pp.77-99
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• 2020

Smart speaker is a device that provides an interactive voice-based service that can search and use various information and contents such as music, calendar, weather, and merchandise using artificial intelligence. Since AI technology provides more sophisticated and optimized services to users by accumulating data, early smart speaker manufacturers tried to build a platform through aggressive marketing. However, the frequency of using smart speakers is less than once a month, accounting for more than one third of the total, and user satisfaction is only 49%. Accordingly, the necessity of strengthening the user experience of smart speakers has emerged in order to acquire a large number of users and to enable continuous use. Therefore, this study analyzes the user experience of the smart speaker and proposes a method for enhancing the user experience of the smart speaker. Based on the analysis results in two stages, we propose ways to enhance the user experience of smart speakers by model. The existing research on the user experience of the smart speaker was mainly conducted by survey and interview-based research, whereas this study collected the actual review data written by the user. Also, this study interpreted the analysis result based on the smart speaker user experience dimension. There is an academic significance in interpreting the text mining results by developing the smart speaker user experience dimension. Based on the results of this study, we can suggest strategies for enhancing the user experience to smart speaker manufacturers.