• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.537-542
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• 2021

Smart TVs provide a variety of services and information compared to existing TVs based on the Internet. In order to provide more personalized services or information, it is necessary to analyze users' viewing patterns and provide customized services or information based on them. The proposed system receives the user's TV viewing pattern, analyzes it, and recommends a TV program or movie as customized information to the user. For this, the system was constructed with a preprocessor and a deep learning model. The preprocessor refines the name of the TV program watched by the user, the date the TV program was watched, and the watched time. Then, the multi-attribute LSTM model trains the refined data and performs prediction.The proposed system is a system that provides customized information to users, and is believed to be a leading technology in digital convergence that combines existing IoT technology and deep learning technology.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.153-167
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• 2015

This paper empirically analyzes specific characteristics of VOD users and their practical viewing patterns, using socio-demographic data of the current IPTV subscribers in combination with actual data of genre usage and payment. The findings revealed active viewing patterns of male, the unemployed, high earners and early adopters. In terms of preferences, households with large numbers of women prefer time shift contents, whereas households composed of more men or preschoolers prefer non-time shift contents. Likewise, the households that have more women or higher income had relatively a lot of experiences of purchasing time shift contents on one hand, but the households characterized by the larger numbers of family members or unemployed householder or young householder showed much willingness to pay premium contents on the other hand. Given the utilization of correct database, the findings offer useful information conducive to service promotion and marketing strategies to maximize VOD use in the practical dimension.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.134-136
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• 2016

최근 TV시청은 다양한 매체를 통해서 이루어지고 있으며, 특히 스마트폰을 통한 시청률이 늘고 있는 상황이다. 광고시장에서도 TV시청 중에 스마트기기를 함께 이용하는 멀티태스킹 사용자가 급증하고 있으며 특히 10~30대의 사용이 적극적이다. TV시청 중 스마트 기기의 사용분야는 메신저, 정보검색, SNS 순이며 스마트 기기사용 내용 중 69%는 시청하던 TV 시청과 관련된 것이었다. 이 중에 75%는 TV에 등장한 제품, 브랜드, 장소에 관한 것이다[1]. TV를 시청하는 상황에 스마트기기의 소셜 활동의 문자를 분석하는 것은 사용자 의도를 파악할 수 있는 의미가 있으며, 시청자의 현재 위치를 파악함으로써 시청자의 의도에 반영되는 상황을 파악할 수 있다. T-Commerce 구매 의도는 사용자의 현재 상황에 대한 순간 의도를 파악하는것이 중요하며, 이와 같은 구매의도를 파악하기 위해서 본 연구에서는 GPS와, Wi-Fi 기반 Fingerprinting 측위기법을 사용하여 특별한 도구나 장비의 설치 없이 현재위치와 멀티태스킹 데이터를 분석하여 구매의도를 파악한다. T-Commerce 소비환경 패턴이 바뀜에 따라, 다양한 소비 환경 데이터 분석은 효율적인 광고 제공과 만족도를 높일 것으로 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.901-902
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• 2009

최근 지상파 TV 방송의 디지털 전환 프로젝트가 본격적으로 진행되고 있다. 디지털 방송 서비스는 다매체, 다채널을 통한 방송 프로그램의 증가와 양방향 TV 방송 서비스로 인해 사용자에게 다양한 방송 프로그램의 선택과 개인별 맞춤형 시청 기회를 제공함으로써 새로운 방송 서비스 환경을 필요로 한다. 본 논문에서는 사용자의 시청 상황을 포함한 시청 패턴을 분석함으로써 시청 패턴 프로파일 및 시청 선호도 연관규칙 생성 기법을 통한 지능적 사용자 맞춤형 DTV 방송 서비스 시스템을 제안한다. 또한, 임베디드 시스템 기반의 사용자 인터페이스를 구현하여 사용자에게 적절한 추천 프로그램을 제공하고, 시청 프로그램 정보에 따른 시청 상황을 자동으로 제어하는 기능을 포함한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.135-136
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• 2018

최근 개인방송은 시청자가 언제 어디서나 원하는 장소, 시간에 시청할 수 있을 정도로 보편적이고 대중화 되어 있다. 일반적으로 시청자는 방송을 시청할 때 영상의 구간별로 진행되는 콘텐츠를 처음부터 시청하면서 확인하거나 댓글을 확인하기 전까지는 확인할 수 있는 방법이 없다. 이에 본 논문은 행동 패턴을 분석하여 해당 영상의 콘텐츠를 파악하여 시청자에게 방송진행자가 무엇을 하고 있는지를 제공할 수 있다. 따라서 시청자가 영상의 구간별 콘텐츠를 확인하여 영상에서 원하는 구간을 선택하여 시청이 가능하게 되면서 시청자에게 영상을 시청하는데 편리성을 줄 수 있는 기대효과를 가진다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.1000-1006
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• 2014

This study focuses on the fact that a OTT service is vigorously used for smart phones, and suggests a design of method to trace the experiences of watching television contents. For this purpose, we developed logging functions and embedded them into existing OTT service application to record flow and pattern of watching context. This paper suggests a log file format which can accurately and precisely record watching actions of users per-second methodology rather than former per-minute methodology. Moreover, this study shows that the application can trace watching attitude according to occurring events by characteristics and playing modes of realtime broadcasting, VOD, advertisement contents. In addition, based on the result of the study, this paper discusses educational, operational meaning of the method such as methodological application in mobile ethnography field or survey for total screening rate.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.99-106
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• 2015

This research is focused on the development of the proprietary database embedded in the OTT device, which is used for searching and indexing video contents, and also the development of the search algorithm in the form of the critical components of the interface application with the OTT's database to provide video query searching, such as remote control smartphone application. As the number of available channels has increased to anywhere from dozens to hundreds of channels, it has become increasingly difficult for the viewer to find programs they want to watch. To address this issue, content providers are now in need of methods to recommend programs catering to each viewer's preference. the present study aims provide of the algorithm which recommends contents of OTT program by analyzing personal watching pattern based on one's history.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1567-1573
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• 2020

The development of Internet technology has brought the era of one-man media. An individual produces content on user own and uploads it to related online services, and many users watch the content of online services using devices that allow them to use the Internet. Currently, most users find and watch content they want through search functions provided by existing online services. These features are provided based on information entered by the user who uploaded the content. In an environment where content needs to be retrieved based on these limited word data, user unwanted information is presented to users in the search results. To solve this problem, in this paper, the system actively analyzes the video in the online service, and presents a way to extract and reflect the characteristics held by the video. The research was conducted to extract morphemes based on the story content based on the voice data of a video and analyze them with big data technology.

• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1357-1364
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• 2010

With the emergence of digital TV broadcasting, various TV channels are provided to a TV audience. But it is getting hard for the audience to find his or her preferred TV programs due to the huge number of TV channels. In order to mitigate the difficulty, channel navigation schemes have been proposed. The schemes predict preferred TV channel from viewer's previous watching histories, and provide the predicted channels when the viewer wants to change TV channel. As channel navigation schemes, there are Most Recently Selected (MRS) scheme providing the most recently watched channel, and Most Frequently Selected (MFS) scheme providing the most frequently selected channel. However, MFS and MRS scheme could be inappropriate to current TV system broadcasting various TV programs in a channel because they are based on the viewer's patterns of watching TV channels. Therefore, this paper proposes an efficient channel navigation scheme, Program Most Frequently Selected (PMFS), based on patterns of watching TV programs instead of TV channels. Simulation results show that the proposed scheme PMFS reduces the seek distance compared to the previous channel navigation schemes, MFS and MRS.

• The KIPS Transactions:PartD
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• v.17D no.6
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• pp.453-462
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• 2010

The switch from analog to digital broadcast television is extended rapidly. The DTV can offer multiple programming choices, interactive capabilities and so on. Moreover, with the spread of Internet, the information exchange between the communities is increasing, too. These facts lead to the new TV service environment which can offer customized TV programs to personal/community users. This paper proposes a 'Customized Digital TV System for Individuals/Communities based on Data Stream Mining' which can analyze user's pattern of TV watching behavior. Due to the characteristics of TV program data stream and EPG(electronic program guide), the data stream mining methods are employed in the proposed system. When a user is watching DTV, the proposed system can control the surrounding circumstances as using the user behavior profiles. Furthermore, the channel recommendation system on the smart phone environment is proposed to utilize the profiles widely.