• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.683-692
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• 2003

The rapid increase of the number of channels makes it hard to find wanted programs from TV. In recent years, the number of channels come up to hundreds with the digital TV arrival. So, it will drive us to the new way of watching TV. In this paper, we introduce a recommendation system for TV programs to overcome this difficulty. We model user profiles and design each module of the system, considering TV environment. Our system gathers basic information from people manually and then updates user profiles automatically by tracking viewing and usage history. As a result, our system recommends daily TV programs based on the changing interest of users. In this paper, we address the problems and solutions by describing our system and the experiment.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.86-93
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• 2012

The emergence of cable TV, satellite broadcasting and IPTV provides viewers with a variety of TV programs. However, viewers' desire for watching their favorite TV program at convenient time has increased because of insufficient spare time. As an increase in smart phone market has accelerated an entry into "the age of smart network media" since 2009, mobile media suggests services connected to other digital devices. Recently, there has been growing interest in TV controling system of smart phone. Therefore, the present study aims to provide an concept of the smart phone application which recommends contents of TV program by analyzing personal watching pattern. To suggest detailed direction of the interaction and UI design, we analyzed previous research and examples of TV controlling applications and products. In addition, public opinion survey was carried out to rationalize this study and suggest suitable UI structure.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.238-252
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• 2009

Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user's preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user's preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user's preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.

• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1453-1462
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• 2010

In this paper, a new program recommendation system is proposed to recommend user preferred VOD program in IPTV environment. A proposed system is implemented with collaborative filtering method. For a user profile which describes user program preference, a program preference, sub-genre preference, and US(user similarity) weight of the user neighborhood is averaged and updated every week. In order to evaluate system performance, real 24-weeks cable TV watching data provided by Nilson Research Corp. are modified to fit for IPTV broadcasting environment and the simulation result shows quite comparative quality of recommendation. The experimental results optimum performance when user similarity based weighting, five person per group and five recommendation programs are used.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.9-19
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• 2010

In this paper, a new program recommendation system is proposed to recommend user preferred VOD program in IPTV environment. A proposed system is implemented with hybrid filtering method that can cooperatively complements the shortcomings of the content-based filtering and collaborative filtering. For a user program preference, a single-scaled measure is designed so that the recommendation performance between content-based filtering and collaborative filtering is easily compared and reflected to final hybrid filtering procedure. In order to provide more accurate program recommendation, we use not only the user watching history, but also the user program preference and sub-genre program preference updated every week as a user preference profile. System performance is evaluated with modified IPTV data from real 24-weeks cable TV watching data provided by Nilson Research Corp. and it shows quite comparative quality of recommendation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.117-120
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• 2012

본 논문에서는 토픽 모델링 기반 TV 프로그램 유사 시청 사용자 그룹핑 및 이를 이용한 TV 프로그램 콘텐츠 추천 알고리듬을 제안하였다. 제안 기술은 토픽 모델링 기법 중 Latent Dirichlet Allocation(LDA) 방법을 이용하여 TV프로그램 시청 기록 내에서 은닉된 유사 사용자들을 그룹핑하고 이러한 유사 시청 사용자 그룹 정보를 이용하여 사용자에게 선호 TV 프로그램 콘텐츠를 자동으로 추천하는 알고리듬이다. 제안된 자동 추천 알고리듬의 성능평가를 위해 실제 TV 시청기록 데이터를 이용하여 훈련 기간과 검증 기간을 나누어 훈련 기간 동안 제안한 알고리듬을 이용하여 사용자 개인에 대한 추천 TV 프로그램 콘텐츠 목록을 생성하여 검증 기간 동안에 실제 추천된 TV프로그램을 얼마나 시청했는지를 측정하여 추천 정확도를 검증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.272-274
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• 2013

최근 TV 시청자들의 콘텐츠 소비량이 증가함에 따라 방송사에서 제공하는 TV 프로그램들의 수량이 방대해지고 장르 또한 다양해지고 있기 때문에 시청자가 TV 프로그램을 선택하는 것이 점점 더 어려워지고 있다. 이러한 문제를 해결하기 위해 TV 프로그램 추천이라는 연구가 활발하게 이루어지고 있다. 기존의 연구에서는 시청자를 기반으로 하는 협업 필터링 추천 방법과 아이템을 기반으로 하는 협업 필터링 추천 방법이 제안되었지만 시청자의 시청 의도를 고려하는 연구는 사례는 적다. 이에 본 논문에서는 LDA 모델링을 이용하여 사용자의 시청 의도를 고려한 TV 프로그램 추천 기법을 제안한다. 실험을 통해 시청자의 시청 의도가 반영된 TV 프로그램 추천이 가능하다는 것을 검증했다.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.211-214
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• 2003

In this paper, we intend to find user's TV program choosing pattern and, recommend programs that he/she wants. So we suggest item dependency map which express relation between chosen program. Using an algorithm that we suggest, we can recommend an program, which a user has not saw yet but maybe is likely to interested in. Item dependency map is used as patterns for association in hopfield network so we can extract users global program choosing pattern only using users partial information. Hopfield network can extract global information from sub-information. Our algorithm can predict user's inclination and recommend an user necessary information.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.214-216
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• 2005

디지털 방송이 시작되면서 시청자가 선택할 수 있는 채널은 200여 개로 늘어났다. 지금처럼 리모컨으로 채널을 돌려가며 보거나 원하는 TV 프로그램을 찾기란 거의 불가능해진 것이다. 이러한 다채널 다매체 시대에 원하는 프로그램 시청을 도와줄 수 있는 프로그램 가이드 시스템의 필요성이 증가하게 되었고, 더 나아가 TV를 시청하는 각 개인의 선호도를 반영하는 것이 요구되었다. 본 논문에서는 r-order Markov Model을 이용한 개인화된 전자 TV 프로그램 추천 시스템을 제안한다. Markov Model은 시간이 지남에 따라 시청하는 프로그램의 변화를 모델링하기 위한 방법으로 사용하였다. 이 시스템은 시청자의 선호 프로그램을 예측하기 위해서 r-order Markov Model을 제안하는 것뿐만 아니라 TV 시청자의 프로그램 선호를 예측하기 위한 모델들을 적용하였다. 실험 결과는 Markov Model이 추천에 대한 높은 정확성을 제공할 수 있다는 것을 보여준다.

• Journal of the Korean Society of School Health
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• v.26 no.2
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• pp.104-113
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• 2013

Purpose: The aim of this study was to investigate prevalence and ecological characteristics of excessive television viewing among elementary school students in Seoul. Methods: Secondary data, representative sample of 11,082 subjects in Seoul was used. After prevalence of the excessive television viewing was identified by using factor analysis, 6 main factors explaining the ecological indicators was founded. After identifying prevalence of the excessive television viewing, by using factor analysis, we found 6 main factors explaining the ecological indicators. After categorizing factors into socio-psychological and lifestyle characteristics, we were investigated the odds ratio of excessive television viewing per characteristics by using multiple logistic regression. Results: The prevalence of excessive television viewing was 29.7% among the elementary school student in Seoul. The prevalence were higher in male, higher grade, and non-South of the Han River. Socio-psychological indicators which expressed excessive television viewing were annoying or bullying, scolding from teacher, depressed mode, thought for a runaway from home, an experience of diet, and negative body image. Life style indicators were a lower intake of fruit and vegetables, a higher intake of Ramyun and soft drink, a worse life style of hand washing, and wearing safety equipment. Conclusion: Lifestyle of television viewing was the comprehensive results from family, school, and society rather than a personal preference. Therefore, it is necessary to educate the hazard of excessive television viewing, to reenforce the students' health competence, to activate a safe leisure program as an alternate of television viewing, and to develop a recommendation.