• Journal of Korea Multimedia Society
• /
• v.13 no.10
• /
• pp.1413-1422
• /
• 2010

With the emergence of digital TV broadcasting, various 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, TV broadcasting companies provide an electronic program guide (EPG), which is a digital guide to scheduled broadcast TV programs. However, it results in the information overload problem and the time-consuming problem since the number of TV channels and programs is gradually on the increase. In this paper, we design and develop a channel filtering system, which recommends a small number of channels by filtering TV channels based on the watching pattern of the TV audience. The channel filtering system does not require the replacement or upgrade of existing TV or set-top box. In addition, it increases usability by skipping the channels that broadcast the audience's non-preferred TV programs while the TV audience presses the channel up/down button.

• Journal of Broadcast Engineering
• /
• v.18 no.1
• /
• pp.88-97
• /
• 2013

Collaborative filtering(CF) for the personalized recommendation is a successful and popular method in recommender systems. But the mainly researched and implemented cases focus on dealing with independent items with explicit feedback by users. For the domain of TV program recommendation in VOD service platform, we need to consider the unique characteristic and constraints of the domain. In this paper, we studied on the way to convert the viewing history of each TV program episodes to the TV program preference by considering the series structure of TV program. The former is implicit for personalized preference, but the latter tells quite explicitly about the persistent preference. Collaborative filtering is done by the unit of series while data gathering and final recommendation is done by the unit of episodes. As a result, we modified CF to make it more suitable for the domain of TV program VOD recommendation. Our experimental study shows that it is more precise in performance, yet more compact in calculation compared to the plain CF approaches. It can be combined with other existing CF techniques as an algorithm module.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.5
• /
• pp.431-436
• /
• 2015

As a number of TV programs broadcast today, researches about TV program recommender system have been studied and many researchers have been studying recommender system to produce recommendation with high accuracy. Recommender system recommends TV program to user by using metadata like genre, plot or calculating users' preferences about TV programs. In this paper, we propose a new TV program Collaborative Filtering Recommender System that exploits viewing time pattern like viewing ratio, relation with finish time and recently viewing history to calculate preference for high-quality of recommendation. To verify usefulness of our research, we also compare our method which utilizes viewing time patterns and baseline which simply recommends TV program of user's most frequently watched channel. Through this experiments, we show that our method very effectively works and recommendation performance increases.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2015.01a
• /
• pp.47-50
• /
• 2015

TV는 타 도메인과 달리, 사전에 정해진 시간에 콘텐츠가 방영된다. 그러므로 TV 프로그램 추천 시스템은 시청자의 현재 시각(time-context)을 고려해야 한다. 시간 기반의 TV 프로그램 추천 방법이 다수 연구되었지만, 대부분의 기존 연구는 특정 시간대(timeslot)에서의 시청자의 선호도를 계산하는 데에만 집중되어 있고, 시청 내역 전체기간에서의 선호도를 고려하지 않은 문제점이 있다. 이러한 문제를 해결하기 위해, 시청자의 지역 선호도와 전역 선호도를 모두 고려한 시간 기반의 TV 프로그램 추천기법을 제안한다. 이를 위해 제안 방법에서는 시간대의 길이에 따라 여러 가지 선호도 모델을 사용한다. 여러 개의 선호도 모델로부터 산출된 선호도를 병합하여 가장 선호도가 높은 TV 프로그램을 추천한다. 실 데이터를 이용한 실험을 통해 기준방식과 비교함으로써, 제안 방법의 효용성을 검증하였다.

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

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

• Journal of Korea Multimedia Society
• /
• v.10 no.1
• /
• pp.128-138
• /
• 2007

With the development of broadcasting technology from analogue to interactive digital, the number of TV channels and contents provided to audience is increasing in a rapid speed. In this multi-media and multi-channel world, it is difficult to adapt to the increase of TV channel numbers and their contents merely using remote controller to search channels. Due to this reason, EPG (Electronic Program Guide) has been one of the essential services providing convenience to audience. So EPG complying with European DVB-MHP specifications, which will be also our domestic standard, is proposed in this paper. In order to provide audiences with DiTV contents they preferred, we apply collaborative filtering algorithm to recommend contents according to preference value of audience group with similar preference. And we use JavaXlet application which is based on MHP to implement this EPG, while the result can be verified by OpenMHP emulator.

• Journal of Broadcast Engineering
• /
• v.14 no.2
• /
• pp.238-252
• /
• 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
• /
• v.12 no.2
• /
• pp.219-230
• /
• 2009

With the development of broadcasting technology from analogue to interactive digital, the number of TV channels and TV contents provided to audiences is increasing in a rapid speed. In this multi-channel world, it is difficult to adapt to the increase of the TV channel numbers and their contents merely using remote controller to search channels. For these reasons, the EPG system, one of the essential services providing convenience to audiences, is proposed in this paper. Collaborative filtering method with multi-step filtering is used in EPG to recommend contents according to the preference of audience groups with similar preference. To implement our designed TV contents recommendation EPG, we prefer DiTV and use JavaXlet programming based on MHP. The European DVB-MHP specification will be also our domestic standard in DiTV. Finally, the result is verified by OpenMHP emulator.

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