• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.964-965
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• 2013

본 연구에서는 사용자의 감성 정보를 기반으로 한 영화 추천 시스템을 설계 및 구현하였다. 이를 위하여 영화 리뷰에서 기본적인 4가지 감성을 뜻하는 단어를 추출 및 분류하고, MovieLens Dataset의 메타데이터에 추가한 후 협업 필터링을 사용하여 영화를 추천한다.

• 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.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.27-28
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• 2014

• Proceedings of the Korea Technology Innovation Society Conference
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• 1999.05a
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• pp.3-10
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• 1999

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.130-139
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• 2009

Most of the movies currently being created are used by CG. As for movie of today, the location of visual effect from film business rose highly with CG introductions to do not ask a style. The CG gets attention recently, and expand a market at rapid pace, the biggest reason are development of software and computer hardware. Advancement of computer technology and technique discharge numerous VFX artists therefore visual effect are possible to deliver with low-budget feature and commercial films more common. As for CG fields Hollywood in United States has most advancement of the computer techniques that create a lot of visual effect artists. However CG techniques of Korea also developed absurdly, because movie market of Korea is getting bigger. Korean CG technology get catching up at the CG techniques of Hollywood every year, however goal of this research in order to study is current address and future possibility of development of Korean CG. The CG example which from the movie was already screened that in the depth. Through movie of existing and knows a different partial authorization, tries to observe the possibility of development of future Korean CG fields.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.471-478
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• 2022

In a good class, the elements that make up the class are organically related as a system. Unilateral assessment without sufficient explanation or agreement on assessment criteria, subjective assessment that does not guarantee the reliability of the assessment process and decolonized evaluation separate from the learning process can be a threat to a good class or healthy learning ecosystem. This study analyzed the evaluation through rubric and its effects to solve problems related to educational evaluation. 'Rubrick' is a descriptive evaluation tool that details the criteria for evaluating performance tasks based on class goals and the quality of performance in several stages. The rubric applied for movie literacy evaluation is 'analytical rubric'. It covers literacy to understand movies, movie making literacy and movie utilization literacy. For rubric, learners recognized it as a valid and very useful learning reflection tool.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.157-171
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• 2014

As electronic word of mouth plays an important role in purchase behavior among consumers, the number of studies on the impact of electronic word of mouth is rapidly increasing. Nevertheless, it is difficult to discover comparative studies on the mass media which had a great impact on consumer's purchase behavior before the impact of electronic word of mouth becomes greater versus the social media where electronic words of mouth are created and distributed. It is considered that it seems to be necessary to find an appropriate mutual supplement point between the media designed for a successful marketing by comparing and analyzing the existing mass media versus the social media, major media for electronic word of mouth. Therefore, this study aims to compare and analyze the impact of comments on movie revenue in the representative forms of mass media such as online news and social media blogs. In particular, this study also considers an appropriate media for promoting movies by period by comparing and analyzing the two media before and after film release. For analysis, this study collects the information on the number of comments on online news and blogs in 70 Korean movies released in 2011 and 2012 from five weeks before film release to eight weeks after film release on a daily basis via Naver. This study also collects the information on the movie revenue using the statistical data of movie industry from Korean Film Commission. As a result of empirical data analysis, it is found that the two media showed no difference in movie revenue before film release, but after film release, the impact of blogs was more significant than that of online news.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.284-286
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• 2021

With the continuous development of the Internet and digital, platforms are emerging to store large amounts of media data and provide customized services to individuals through online. Companies that provide these services recommend movies that suit their personal tastes to promote media consumption. Each company is doing a lot of research on various algorithms to recommend media that users prefer. Movies are divided into genres such as action, melodrama, horror, and drama, and the film's audio (music, sound effect, voice) is an important production element that makes up the film. In this research, based on movie trailers, we extract audio for each genre, check the commonalities of audio for each genre, distinguish movie genres through supervised learning of artificial intelligence, and propose a utilization method for generating metadata in the future.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.443-448
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• 2019

본 논문은 공공 데이터 Open API와 TMDB(The Movie Database) API를 이용하여 사용자의 선호 영화를 Google에서 제공해주는 Tensoflow로 인공신경망 딥러닝 학습하여 사용자가 선호하는 영화를 맞춤 추천하는 애플리케이션의 설계 및 구현에 대하여 서술한다. 본 애플리케이션은 사용자가 쉽게 영화를 추천받을 수 있도록 만들어진 애플리케이션으로 기존의 필터링 방식으로 추천하는 방식의 애플리케이션들과 달리 사용자의 취향을 딥러닝 학습을 통해 최적의 영화 Contents를 추천함과 아울러 기존 영화의 특성을 학습하여 흥행할 신규 영화를 예측하는 기능 또한 제공한다. 본 애플리케이션에 사용된 신규 영화 흥행 예측 모델은 약 85%의 정확도를 보이며 사용자 맞춤추천의 경우 기존 장르 추천이나 협업 필터링 추천보다 딥러닝을 통한 장르, 감독, 배우 등의 보다 세밀한 학습 추천이 가능하다.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.2
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• pp.41-54
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• 2019

The cinema circuit is facing a digital, network, and mobile age, which expands non-theater accessibility to movies. Application platforms are situated as the most competitive business model that provide digital content such as games, music, books, and movies. Consumers can acquire content-related information not just offline, but online as well. Therefore, item information provided by application platforms is required. The information provided by application platforms consists of richly descriptive information such as storyline summary, consumer reviews, and related articles, while non-descriptive normative information covers data such as sales ranking, release date, genre, rental or purchase cost, domestic/foreign classification, consumer rating, number of consumer ratings, film rating, and so on. In this study, we surveyed and analyzed statistically the correlation between real-time sales ranking and other comparable non-descriptive information.