• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2001.11b
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• pp.29-33
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• 2001

사용자에게 적합한 프로그램을 추천하거나 필터링을 수행하는 지능형 방송 단말 응용 소프트웨어 에이전트에서 필수적으로 사용되는 사용자 선호도 (User Preference)를 추출하는 알고리즘을 연구하였다. 시청자 선호도 추출 알고리즘으로는 시청자의 프로그램 장르나 출연 배우 등에 대한 선호도, 프로그램 시청 시간 등에 대한 선호도나 시청 프로그램에 대한 사용자의 인터랙션 습성 분석에 의한 프로그램 선호도 등을 probabilistic framework과 rule-based framework을 근간으로 추출하는 알고리즘 연구에 대한 결과를 본 논문에서 제시한다.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.3
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• pp.269-295
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• 2009

The metadata elements of TV programs were designed based on PBCore (Public Broadcasting Metadata Dictionary), which is useful descriptive metadata for TV programs, and on interview results with TV program records managers. TV program information was visualized to extract core information and build a proper schema. Through this analysis, detailed metadata elements were designed in four representative areas: intellectual content, intellectual property, instantiation and extensions.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2004.11a
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• pp.151-154
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• 2004

디지털 방송의 시작과 함께, 지상파, 위성, 케이블과 같은 다양한 매체를 통한 다채널 방송 시청 환경의 도래는 사용자에게 많은 방송 프로그램 시청 정보를 전달하게 되었다. 이와 더불어, 방송 단말에 전송된 다양한 방송 프로그램 정보를 탐색하고 선호 방송 프로그램을 선별하기 위해서는 사용자에게 많은 노력이 요구된다. 이러한 요구에 따라, 똔 논문에서는 다채널 방송 시청 환경 하에서 사용자의 방송 프로그램 시청 히스토리를 분석하고, 특정 시간에 따른 사용자의 방송 프로그램 시청 패턴을 추출하여 방송 프로그램 장르에 대한 사용자 선호도를 자동으로 계산하는 알고리즘을 제안하고, MPEG-7 MDS 구조에 따른 사용자 선호도 서술과 사용자의 선호도에 따라 방송 프로그램을 자동적으로 추천하는 TV 프로그램 추천 어플리케이션을 소개한다 본 실험을 위해 실제 연령대별, 성별, 시간대별로 사용자의 TV 시청 자료를 사용하였으며, 실험결과를 통해 본 논문에 제안된 베이시안 네트워크 기반 사용자 자동 학습 알고리즘이 효과적으로 사용자 선호도를 학습할 수 있음을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.461-464
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• 2020

지상파 방송에서는 청각장애인을 위해 폐쇄자막(closed caption) 서비스가 제공되고 있다. 현재의 폐쇄자막 방송은 속기사가 실시간으로 방송을 보면서 입력하기 때문에 지연이 있다. 또한 이렇게 입력된 폐쇄자막은 TV 프로그램 영상과 별도로 저장되기 때문에 영상과 그 시작점이 맞지 않는 경우가 대부분이다. 폐쇄자막을 온라인 서비스 등에 제공하고자 할 때 이러한 문제로 인해 영상과의 동기가 맞지 않아 사용이 어렵다. 본 논문에서는 TV 프로그램의 음성을 인식하여 동기화된 텍스트를 추출하고, 이를 기 저장된 폐쇄자막과 정렬하여 동기화하는 방법을 제안한다. 실제 TV 프로그램과 자막에 적용하였을 때 대부분의 음절과 라인에서 동기화가 정확히 이루어짐을 확인하였다.

• Journal of Digital Contents Society
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• v.13 no.2
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• pp.197-203
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• 2012

Although the increase in number of the programs provides rich entertainment to viewers, it also has caused a negative of making it hard for viewers to find out their favorite programs. To address the problem, several researches have been performed mainly focusing on the technologies to analyze a viewer's TV watching-patterns and to recommend a program (or a channel) based on the analysis when a viewer changes channels. The researches, however, have the trouble of frequently failing to choose proper programs because, in the real-world broadcasting circumstance, the programs are re-broadcast over a number of the channels and a set of programs of a genre are usually playing in the overlapped times. To avoid the trouble, the data service, proposed in the paper, allows a viewer to book "explicitly" his/her favorite programs and provides a set of functions of listing up the booked program's broadcasting schedules and reserving viewing or recording the booked programs.

• Journal of Digital Convergence
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• v.9 no.5
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• pp.21-30
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• 2011

Broadcasting contents market is changing back of a smart TV appearance recently. This study proposed a contents industry view and government policy direction. We accomplished an depth interview with expert of Broadcasting Content Production Support. The data analysis method executed the content analysis. The research results presented in a three direction.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.94-101
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• 2000

User adaptive reception of broadcast programs includes the functionality such as the user adaptive filtering and browsing functionality. The user adaptive filtering means that the user can limit the list of programs to include only his/her favorite programs among hundreds of available programs. The user adaptive browsing means that the user can view a short summary of his/her selection in the way that he/she prefers. When the receiving system include the random access storage device, the automatic recording functionality of users favorite programs can be included. The user adaptive reception requires support from various meta-data such as user preference data and content description data. TV Anytime forum is a standardization effort to enable user adaptive TV reception, which means that the user can watch what s/he wants when s/he want in the way s/he wants. MPEG-7 includes not only the content description for broadcast applications but also other content descriptions such as structure information. This paper addresses the relationship between MPEG-7 and TV Anytime and investigates how MPEG-7 should be designed and be used to satisfy the requirements of the user adaptive reception of broadcast program.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.149-155
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• 2019

In order to let the world know a real China, China has been strengthening the cultural pursuit of radio and TV programs and accelerating the development of cultural TV programs. By developing cultural TV programs, we can maintain the characteristics of national culture, publicize Chinese traditional culture and introduce the time-honored Chinese civilization to the world. Television can not only keep people informed of current events and social issues, but also provide people with colorful cultural life and entertainment. It is an innovation to spread cultural programs by means of entertainment. TV programs convey relevant cultural contents to the audience through various forms so as to improve the audience's cultural literacy and social influence. This paper summarizes the development background and current situation of Chinese TV culture, and gives some thoughts on the development of current cultural programs, which can provide some references for the communication and development of Chinese cultural TV programs.

• The Journal of the Korea Contents Association
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• v.8 no.3
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• pp.75-85
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• 2008

Since the first interactive broadcasting of Skylife had been launched in S. Korean it has passed over 4 years. Subsequently the new digital broadcasting platforms, such as DMB, Mobile-TV, IPTV and so forth, are appearing between the keen rivalries. At the centre of diagram for new digital platforms there is positioned the interactivity. In order to cope with the limitation of contents on television, even Korean terrestrials have resolutely carried out interactive broadcasting as of 8th of June, 2006. Although the flamed competition of digital environment there are lacking in the public understanding and interests for interactive programmes on digital television. We can be analysed that it is difficult to acquire the viewer's satisfaction for guessability in spite of our governmental efforts. Here it presents the problematic results obtained by analysing both quantitative and qualitative data from the usability test for the current interactive programme in centre of the viewer.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.270-283
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• 2012

With the advent of multi-channel TV, IPTV and smart TV services, excessive amounts of TV program contents become available at users' sides, which makes it very difficult for TV viewers to easily find and consume their preferred TV programs. Therefore, the service of automatic TV recommendation is an important issue for TV users for future intelligent TV services, which allows to improve access to their preferred TV contents. In this paper, we present a recommendation model based on statistical machine learning using a collaborative filtering concept by taking in account both public and personal preferences on TV program contents. For this, users' preference on TV programs is modeled as a latent topic variable using LDA (Latent Dirichlet Allocation) which is recently applied in various application domains. To apply LDA for TV recommendation appropriately, TV viewers's interested topics is regarded as latent topics in LDA, and asymmetric Dirichlet distribution is applied on the LDA which can reveal the diversity of the TV viewers' interests on topics based on the analysis of the real TV usage history data. The experimental results show that the proposed LDA based TV recommendation method yields average 66.5% with top 5 ranked TV programs in weekly recommendation, average 77.9% precision in bimonthly recommendation with top 5 ranked TV programs for the TV usage history data of similar taste user groups.