• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.451-454
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• 2016

최근 스마트폰의 보급률이 높아짐에 따라, 사용자들이 스마트폰을 사용하여 컨텐츠를 즐기는 시간이 많아졌다. 이후 모바일 웹에서 서비스되는 만화들이 연달아 대중들의 이목을 끌게 되어 네이버 웹툰, 다음 웹툰 등 웹툰 서비스 및 웹툰 플랫폼이 증가하고 있다. 또한 웹툰 데이터의 가치와 신뢰성도 점점 높아지고 있어, 영화 애니메이션 게임 등 콘텐츠 사업에 많은 데이터가 사용되고 있다. 따라서 본 논문에서는 나이, 성별, 선호 카테고리, 선호 웹툰 플랫폼 등과 같은 개인 성향 기반으로 협업 필터링 방법을 적용하고, 웹툰의 방대한 데이터를 효과적으로 관리하기 위해 클라우드 컴퓨팅 시스템인 AWS(Amazon Web Service)를 이용하여 개인 성향에 맞게 웹툰을 추천해주는 웹툰 추천 시스템을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1180-1183
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• 2015

최근 스마트폰 사용자의 증가와 함께 무선 인터넷 보급률이 높아지면서, 언제 어디서나 위치에 구애받지 않고, 네이버 웹툰, 다음 웹툰 등으로 실시간 웹툰 서비스의 이용이 증가하고 있다. 웹툰데이터의 가치와 신뢰성도 점점 높아지고 있어, 각종 영화 애니메이션 게임 등 콘텐츠 사업에 많은 데이터가 사용되고 있다. 본 논문에서는 나이, 성별, 선호 카테고리, 선호 웹툰 플랫폼 등과 같은 개인 성향을 이용하여 협업필터링 방법을 적용하고, 기존에 웹툰에 대한 리뷰를 개인 감정에 관련된 온톨로지를 이용하여 각각의 사용자들이 보고 싶어하는 웹툰을 자동적으로 추천해주는 웹툰 추천 시스템을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.905-908
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• 2015

최근 한국의 와인 소비 트랜드는 매년 약 10%의 성장률을 보일 만큼 빠르게 주목 받고 있다. 그러나 와인과 관련된 정확한 데이터의 부재와 이를 전문적으로 주관하는 기관이 없는 현실 등 국내 와인 시장의 여러 문제점이 언급되고 있다. 이 문제를 해결하기 위해서는 와인 소비자의 직접적인 데이터가 수집 가능해야 하며 관련 데이터를 정리, 분석할 수 있는 시스템이 필요할 것이다. 본 연구에서는 바코드 인식 기술을 이용하여 보다 정확한 사용자의 데이터를 수집하고 와인 선호도를 추출하는 Top-k sky-line algorithm을 적용하여 데이터를 효과적으로 분석 및 통계를 하는 데에 목적을 둔다. 이 방법은 데이터를 수집, 분석할 뿐만 아니라 와인을 선별하고 사용자의 선호도를 기반으로 와인을 추천해 줄 수 있다는 점에서 매우 효과적일 것으로 사료된다. 본 연구에서는 기획의도 및 동기, 관련 연구 및 응용, 제안하는 방법, 예상 콘텐츠 시나리오, 기대효과, 결론을 소개하고자 한다.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• 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.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.47-57
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• 2009

Collaborative tagging systems allow users to attach tags to diverse sharable contents in social networks. These tags provide usefulness in reusing the contents for all community members as well as their creators. Three-dimensional data composed of users, items, and tags are used in the collaborative tag-based recommendation. They are generally more voluminous and sparse than two-dimensional data composed of users and items. Therefore, there are many difficulties in applying existing collaborative filtering methods directly to them. Latent factor models, which are also successful in the area of collaborative filtering recently, discover latent features(factors) for explaining observed values and solve problems based on the features. However, establishing the models require much time and efforts. In order to apply the latent factor models to three-dimensional collaborative filtering data, we have to overcome the difficulty of establishing them. This paper proposes various methods for determining preferences of users to items via establishing an intuitive model by assuming tags used for items as latent factors to users and items respectively. They are compared using real data for concluding desirable directions.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.135-141
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• 2011

Mobile communication technology in the field of ubiquitous computing is one of important technologies. The development of GPS technology in mobile communications technology and PDA, the vast majority of mobile devices such as smart phones also being equipped with GPS functionality. This user where they are located where they are based on the number of through services were able to receive information. In this paper, mounted on a mobile device user's location using GPS capabilities to track the migration routes and the accumulated user to determine the migratory path of the system used to recommend the proposed route of the proposed method and system Designs. The services offered by tracking the location of the user to move the user to route data to create and upload to the server. Upload a migration path to move data from where they currently reside to the user is recommended to go to your destination. The services offered by the users do not know where to travel or go to your destination, etc. If you do not remember the path can be useful.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.23-30
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• 2010

It is important for the strategy of cosmetic sales to investigate the sensibility and the preference degree in the environment that the makeup style has been changed focusing on the consumer center. We proposed the human sensibility ergonomics makeup recommendation system (MakeupRS) using the context sensor information applying the collaborative filtering technique as one of methods in the makeup style development centered on the consumer's sensibility and the preference. In the collaborative filtering technique, the Pearson correlation coefficient applying to the case amplification is used to calculate similarity weights between the users. To investigate the sensibility according to the effect of makeup styles, the makeup styles were analyzed in terms of 6 style factors, such as, the foundation, the color lens, the eye shadow, the eye lash, the cheek brusher, and the lipstick. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the human sensibility ergonomics makeup recommendation system.

• The Journal of the Korea Contents Association
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• v.16 no.1
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• pp.611-619
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• 2016

Prior researches on brand authenticity so far have focused on the scale development for measuring brand authenticity. However, at the point of time it is necessary to consider the utilization of the proposed scale in an integrative approach. Accordingly, this study aims to examine brand authenticity mediated the effect of brand authority and ethicality on purchase and word-of-mouth intention. The survey method was used for this paper, and data from a total of 136 office workers were used for the analysis. And structural equation model was used to analyze the data. The results of this empirical study is summarized as followings. First, brand authority and ethicality do not have a direct effect on purchase and word-of-mouth intention but brand authenticity mediates the effect of brand authority and ethicality. Second, brand authority and ethicality have positive effect on brand authenticity. This study provides information on the purchase and word-of-mouth intention of salary man. Further, it will provide meaning suggestion point of the importance of brand authenticity in establishing the policy of brand management.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.307-316
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• 2016

A user usually gets the same suggesting results as everyone else in most of the multimedia social services, nowadays. To address the challenging problem of personalization in the social network, we propose a method which exploits user's activities, user's moods, and user's friend relationships from the social network to build a decision-making system. Depending on a current state of the user's mood, this system infers the most appropriated video for the user. In the system, the user evaluates a set of the given recommendation methods which extract from the user's database social network and assigns a vague value to each method by a weight. Then, we find the fuzzy collection solution for the system and classify the set of methods into subsets, and order the subsets based on its local dominance to choose the best appropriate method. Finally, we conduct an experiment using the YouTube API with a lot of video types. The experiment result shows that the channel recommendation system appropriately affords the user's character, it is more satisfying than the current YouTube based on an evaluation of several users.