• Annual Conference of KIPS
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• 2017.11a
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• pp.817-819
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• 2017

최근 스마트 기기를 이용하여 의상을 추천하는 시스템에 대한 연구가 활발하게 진행되고 있다. 하지만 기존 연구들은 의상 판매를 목적으로 하거나, 지속적으로 전문가의 견해를 업데이트 해줘야 하는 번거로움을 가지고 있다. 본 논문에서는 트렌드 고려가 어려운 전문가 추천시스템 위주의 의상 추천 시스템의 단점을 보완하려했다. 콘텐츠 기반 추천 알고리즘과 개개인의 코디에 대한 빈도수 분석을 통해 개개인의 성향을 고려했으며, 계층적 클러스터링 알고리즘을 이용하여 군집화 된 유사 사용자들의 코디들을 토대로 트렌드를 반영했다.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.845-852
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• 2018

This study has developed a preventive launcher from smartphone addiction for the digital generation and the contents recommendation based on machine learning which used multiple and collective intelligence. This could provide convenient digital nurturing experience for the parents who fear their children's over use of digital devices and also suggest individually adaptive digital learning methods that enhance the learning efficiency and pleasurable and safe learning environment for the children. Suggested application is a kind of gamification launcher that protects children from harmful contents and from smartphone addiction with time limit settings. For parents who find difficulty choosing from various kinds of contents and applications for education, this suggested system could provide a learning analytic report based on big data after collecting and analyzing the data of their children's learning and activities and recommend contents necessary for their kids using recommended algorithm by collective intelligence.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.1-10
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• 2021

This study empirically confirmed 'the political bias of the YouTube recommendation algorithm' and 'the selective exposure of user' to verify the Filter Bubble phenomenon of YouTube. For the experiment, two new YouTube accounts were opened and each account was trained simultaneously in a conservative and a liberal account for a week, and the "Recommended" videos were collected from each account every two days. Subsequently, through the text mining method, the goal of the research was to investigate whether conservative videos are more recommended in a righties account or lefties videos are more recommended in a lefties account. And then, this study examined if users who consumed political news videos via YouTube showed "selective exposure" received selected information according to their political orientation through a survey. As a result of the Text Mining, conservative videos are more recommended in the righties account, and liberal videos are more recommended in the lefties account. Additionally, most of the videos recommended in the righties/lefties account dealt with politically biased topics, and the topics covered in each account showed markedly definitive differences. And about 77% of the respondents showed selective exposure.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.133-146
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• 2022

Despite the fact that Internet users around the world watch YouTube every day, very few users accurately recognize the recommendation algorithm for search results, and Google and YouTube are not disclosing it. Researchers tried to explore the undisclosed algorithm of YouTube in a reverse engineering design method, find key factors, and check the logical structure in which media platform operators recommend keyword search results and arrange them on the screen. Therefore, researchers studied the basic content priority factors through several months of discussion and data collection, and tried to reverse engineer the influencing factors based on the recommendation results according to male and female gender among the collected keyword search results. Although researchers' design only analyzed some of the almost infinite level of data uploaded and viewed for more than hundreds of hours every hour, these exploratory attempts will study media platform algorithms in the future, understand the intentions of operators, and protect users. thought it could be done.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.437-442
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• 2021

In this study, a recommendation algorithm was implemented by learning a deep learning-based classification model for consumer data. For this purpose, a meaningful result is presented as a result of learning using ResNet50, which is commonly used in classification tasks by converting user data into images.

• Annual Conference of KIPS
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• 2022.05a
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• pp.346-348
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• 2022

Media Contents Recommendation Web Service (service name 'mobodra') is a web service that analyzes media types and genre tastes for each user and recommends content accordingly. Users select some of the works randomly provided on the web when signing up for membership and analyze their tastes based on this. Based on this analysis, preferred content for each user is recommended. In this paper, we implement a content recommendation algorithm through item-based collaborative filtering. When the user's activity data or preference is re-examined, the above process is executed again to update the user's taste.

• Annual Conference of KIPS
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• 2022.05a
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• pp.28-30
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• 2022

장애인은 정보 접근 제한과 편의시설 부족의 요인 등으로 인하여 여행 참여율이 낮아 비장애인과 동일한 여행 서비스를 경험하기에 어려움이 있다. 본 연구에서는 장애 요소 조건을 기반으로 가중기반의 알고리즘에 따라 콘텐츠를 추천하는 서비스를 제안하고 이에 따른 애플리케이션을 구현한다. 기존의 흩어져있던 여행지에 관한 정보와 지도 Open API를 이용하여 여행지에 대해 추천을 하는 애플리케이션 개발을 기획한다. 비장애인은 가중 조건과 관계없이 장애인의 경우에는 장애의 분류, 그 경도에 따라 여행 시 고려해야 할 조건에 따른 가중치를 두어 여행콘텐츠를 상단에 띄어 추천해 주는 방식에 대한 연구 결과를 제공하여 누구든 비장애 여행이 가능해지도록 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.99-105
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• 2021

In addition to the OTT video production service represented by Nexflix and YouTube, a personalized recommendation system for content with artificial intelligence has become common. YouTube's personalized recommendation service system consists of two neural networks, one neural network consisting of a recommendation candidate generation model and the other consisting of a ranking network. Netflix's video recommendation system consists of two data classification systems, divided into content-based filtering and collaborative filtering. As the online platform-led content production is activated by the Corona Pandemic, the field of virtual influencers using artificial intelligence is emerging. Virtual influencers are produced with GAN (Generative Adversarial Networks) artificial intelligence, and are unsupervised learning algorithms in which two opposing systems compete with each other. This study also researched the possibility of developing AI platform based on individual recommendation and virtual influencer (metabus) as a core content of OTT in the future.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.501-518
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• 2017

Reducing the time it takes for researchers to acquire knowledge and introduce them into research activities can be regarded as an indispensable factor in improving the productivity of research. The purpose of this research is to cluster the information usage patterns of KOSEN users and to suggest optimization method of personalized recommendation service algorithm for grouped users. Based on user research activities and usage information, after identifying appropriate services and contents, we applied a Spark based big data analysis technology to derive a personal recommendation algorithm. Individual recommendation algorithms can save time to search for user information and can help to find appropriate information.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.56-66
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• 2022

Due to the recent increase in information encountered on social media, algorithm-based recommendation formats selectively provide information based on user information, which often causes a filter bubble effect by an Echo Chamber. Eco-chamber refers to a phenomenon in which beliefs are amplified or strengthened by communication only in an enclosed system, and filter bubbles refer to a phenomenon in which information providers provide customized information according to users' interests, and users encounter only filtered information. The purpose of this study is to propose a method of efficiently selecting information as a way to improve the filter bubble phenomenon by such an echo chamber. The research progress method analyzed recommended algorithms used on YouTube, Facebook and Amazon. In this study, humanities solutions such as training critical thinking skills of social media users and strengthening objective ethical standards according to self-preservation laws, and technical solutions of model-based cooperative filtering or cross-recommendation methods were presented. As a result, recommended algorithms should continue to supplement technology and develop new techniques, and humanities should make efforts to overcome cognitive dissonance and prevent users from falling into confirmation bias through critical thinking training and political communication education.