TV Users Research Articles

스마트 TV 상의 개인화된 콘텐츠 추천을 위한 지능형 시스템 설계 및 구현

최근 스마트 TV는 빠른 속도로 확산되고 있으나 스마트 폰이나 스마트 패드에 비하여 입력장치의 사용이 매우 불편하며 많은 양의 콘텐츠 중 자신에게 적합한 콘텐츠를 빠르게 찾기 어렵다는 한계로 인하여 스마트 기기 중 그 기능의 사용빈도가 낮다는 문제점이 있다. 본 연구에서는 이러한 문제점을 개선하고 스마트 TV 상에서 사용자가 원하는 콘텐츠를 보다 쉽게 찾을 수 있는 개인화된 지능형 추천 시스템을 제안하고 구축하고자 한다. 사용자 선호정보를 이용한 프로파일 분석, TV 시청에 대한 패턴 분석, 애플리케이션에 대한 사용통계 분석 등 사용자에 대한 여러 가지 정보를 다차원적으로 고려하여 사용자에게 가장 적합한 콘텐츠를 추천할 수 있는 개인화된 지능형 시스템을 설계 및 구축하였다. Recently, smart TVs have widely spread in our daily lives. However, it is difficult for users to find proper TV contents among a lot of TV and application contents because of inconvenience of input devices compared with those of smart phones or smart pads, so there are some problems with very low utilization of the smart functionalities of smart TVs. We suggest a personalized contents recommender system on smart TVs to resolve these problems and help for users to search appropriate contents easily and quickly in this research. We design and implement an intelligent system for personalized contents recommendation on the smart TVs based on multi-dimensional analysis considering user profiles and preferences, watching patterns of TV programs, and TV contents use statistics of TV users.

Exploiting content relevance and social relevance for personalized ad recommendation on internet TV

There have been not many interactions between the two dominant forms of mass communication: television and the Internet, while nowadays the appearance of Internet television makes them more closely. Different with traditional TV in a passive mode of transmission, Internet TV makes it more possible to make personalized service recommendation because of the interactivity between users and the Internet. In this article, we introduce a scheme to provide targeted ad recommendation to Internet TV users by exploiting the content relevance and social relevance. First, we annotate TV videos in terms of visual content analysis and textual analysis by aligning visual and textual information. Second, with user-user, video-video and user-video relationships, we employ Multi-Relationship based Probabilistic Matrix Factorization (MRPMF) to learn representative tags for modeling user preference. And then semantic content relevance (between product/ad and TV video) and social relevance (between product/ad and user interest) are calculated by projecting the corresponding tags into our advertising concept space. Finally, with relevancy scores we make ranking for relevant product/ads to effectively provide users personalized recommendation. The experimental results demonstrate attractiveness and effectiveness of our proposed approach.

The Countermeasures of Improving the Willingness to Pay for the Pay Channel

Along with common use of the digital technology, the development of digital television industry has become the trend of the world. In July 2003, the State Administration of Radio came up with the conversion concept of digital television and approved 33 cities as the first batch of cable digital TV pilot. Since then, the number of cable television subscribers in China has been growing at a high speed, according to the data of the State Administration of Radio and Television. Until the end of January of 2011, China’s digital cable TV users has exceeded 90,000,000, reaching 90,392,000. The degree of wired digitalization reached 48.26%. (The basic users of cable TV were 187,300,000. China digital cable TV users increase steadily, and the level of cable digitalization rises steadily. However, the development of the digital pay channel of China is not so satisfactory. We should pay attention to some problems, for example, the purchase intention of pay channel is not so high in China. Here we are going to summarize the reason that the purchase intention of pay channel is not so high. Besides, we are going to try to find out the countermeasures.

The effect of flow experience on user adoption of mobile TV

The wide broadband of third generation (3G) mobile communication technologies enables advanced mobile data services such as mobile TV. However, users’ adoption intention of mobile TV may decrease with their poor experience caused by the lack of usability, such as small screens and slow response. Drawing upon the flow theory, this research examined mobile TV user adoption. The results indicated that perceived ease of use, access speed and content quality have significant effects on the flow experience, which involves three dimensions: perceived enjoyment, perceived control and attention focus. In turn, flow experience affects perceived usefulness and usage intention. Thus, service providers need to present an engaging experience to users in order to facilitate their adoption and usage of mobile TV.

Automatic and personalized recommendation of TV program contents using sequential pattern mining for smart TV user interaction

Due to the excessive number of TV program contents available at user's side, efficient access to the preferred TV program content becomes a critical issue for smart TV user interaction. In this paper, we propose an automatic recommendation scheme of TV program contents in sequence using sequential pattern mining (SPM). Motivation of sequential TV program recommendation is based on TV viewer's behaviors for watching multiple TV program contents in a row. A sequence of TV program contents for recommendation to a target user is constructed based on the features such as an occurrence and net occurrence of frequently watched TV program contents from the similar user group to which the target user belongs. Three types of SPM methods are presented--offline, online and hybrid SPM. To extract sequential patterns of preferably watched TV program contents, we propose a preference weighted normalized modified retrieval rank (PW-NMRR) metric for similar user clustering. In the offline SPM method, we effectively construct the sequential patterns for recommendation using a projection method, which yields good performance for relatively longer sequential patterns. The online SPM method mines sequential patterns online by effectively reflecting the recent preference characteristics of users for TV program contents, which is effective for short-sequence recommendation. The hybrid SPM method combines the offline and online SPM methods. The maximum precisions of 0.877, 0.793 and 0.619 for length-1, -2 and -3 sequence recommendations are obtained from the online, hybrid and offline SPM methods, respectively.

Interactive TV user interfaces: how fast is too fast?

The analysis presented in this paper is the search for optimal scrolling speeds of the user interface for interactive TV (iTV) navigation. The most popular navigation techniques for browsing through user interfaces usually incorporate scrolling. Furthermore, the latest set-top-boxes have enough processing power to support very fast scrolling, not only for texts, but also for photographs and other elements. We designed and performed an experiment to measure the optimal scrolling speed for different activities on a user interface. We found that the optimal speed depends on the type and complexity of the navigational elements (like text or graphics). We discovered that less-complex elements, such as numbered TV-channel names, tolerate much higher scrolling speeds than more-complex elements, such as graphical elements like movie posters. We also analyzed the impact of the direction of navigation (vertical, horizontal) on the optimal speeds and the results show no major impact on the graphical elements, while for the text elements the difference is obvious. The results of this study can improve the usability of the horizontal and vertical navigation techniques in modern iTV navigation.

New Value Creation in the TV Home Shopping Industry - Focused on Offline Shopping and Home Shopping, TV User Experience -

New Value Creation in the TV Home Shopping Industry - Focused on Offline Shopping and Home Shopping, TV User Experience -

Adoption of smart TVs: a Bayesian network approach

PurposeThis study aims to identify factors that determine the smart TV buying decisions of users and analyze the relationships among the factors by using Bayesian network approach.Design/methodology/approachThis study investigates smart TV users' perception based on innovation diffusion theory (IDT) which includes five innovation attributes: relative advantage, compatibility, complexity, trialability, and observability. The authors employ Bayesian network to identify causal relationship among the innovation attributes and analyze the sensitivity of the intentions to changes in factors.FindingsThe results show that relative advantage has the greatest influence on the purchase intention of smart TV, followed by compatibility, entertainment, web‐browsing and n‐screen.Research limitations/implicationsThe reliability of the results is limited as the survey is not carried out on a large number of samples. The study, however, suggests a future direction for smart TV in consumers' point of view.Practical implicationsAccording to the findings, companies should focus on enhancing relative advantage, rather than other attributes and entertainment service, to encourage the adoption of smart TV.Originality/valueSmart TV is an evolving technology in the phase of market introduction. The definition and characteristics of smart TV are still uncertain. The previous literatures, however, were focused on the contents of smart TV and service, restructuring of broadcasting industry, and changes in the competitive landscape. The consumers have not been discussed in detail yet. This paper's contributions are twofold: first, it identifies important attributes for the adoption of smart TV in consumers' intention; second, it suggests a new methodology of Bayesian network in determining consumer buying factors.

6.1: Influence of TV Media Content on Display Lifetime and Image Sticking Measurement Techniques

AbstractBoth lifetime and image sticking measurement techniques are well established but are somewhat uncharacteristic of actual TV usage. The purpose of this paper is to quantify TV usage under various media categories and propose modifications to the standard experimental techniques to better represent the usage demands from TV media.

Editorial for automated media analysis and production for novel TV services

Welcome to this special issue on Automated Media Analysis and Production for Novel TV Services (AIEMPro). Modern media is characterised by a significant increase in the amount of digital video content (digital TV channels in particular), and the diversification of broadcast possibilities and storage and consumption devices. This phenomenon has recently given rise to the emergence of many new services and novel TV programmes consumption schemes and usage trends (TV-on-demand, interactive TV, Personal Video Recorders, Catch-Up TV), aimed at making broadcast content available to consumers according to their needs, i.e. without any constraint on location and/or time and with the possibility to cherry pick and navigate according to the viewers desire. These services have been already proven to be very effective in significantly increasing the content audience and open new niches for profitable use of broadcast content. However, this new operational context naturally brings a side effect: many more digital media items need to be produced by the same or reduced staff, and in a faster and more costefficient way. In addition, the new media distribution and consumption paradigms and the corresponding new production workflows based on digital computer-based tools require an immediate revision of the traditional ways of making business in media industry, by substituting the traditional oneto-many broadcasting model with a novel framework in which social media and interactivity play a major role. Multimed Tools Appl (2013) 63:281–286 DOI 10.1007/s11042-012-1083-3

A Conceptual Model Elucidating the Relationships between Television and Childhood Health Outcomes

The IOM's L.E.A.D. framework (Locate, Evaluate, and Assemble evidence to inform Decisions) guided a comprehensive analysis of research examining links between TV (amount, content) and child health outcomes, which resulted in the development of a novel conceptual model expressing these links and strengths of relationships. Terms searched in CINAHL, PubMed, and PsychInfo databases (children, food marketing, BMI, television, media) yielded 39 articles. Research suggests links between TV usage and physical health (BMI, eating disorders, hypercholesterolemia, hypertension, asthma, sleep disorders), psychological health (depression, mood disorders), behavior (violence, aggression, substance abuse), and learning. TV's contribution to BMI include calorie conservation via displacement of energy expending activities and increased energy intake by promoting more eating occasions featuring calorie‐dense foods. Interventions aiming to reduce TV viewing and children's BMI have mixed results indicating a need to help parents understand TV's role in children's health and the importance of establishing household policies regulating TV viewing time. This study's conceptual model can be used by health promotion and nutrition communication professionals to visually convey to parents the health consequences of TV viewing to increase their understanding of how TV affects child health.

LDA 기반 은닉 토픽 추론을 이용한 TV 프로그램 자동 추천

다채널 TV, IPTV 및 Smart TV 서비스의 등장으로 인해 수많은 방송 채널과 방대한 TV 프로그램 콘텐츠가 시청자 단말로 제공됨으로써 시청자들은 자신이 원하는 콘텐츠를 쉽게 찾고 소비하는 것이 어려운 TV 시청 환경을 맞게 되었다. 따라서 TV 사용자들에게 자신이 선호하는 콘텐츠를 자동 추천해 줌으로써 원하는 콘텐츠로의 접근성을 증대시키는 것은 미래의 지능형 TV 서비스에 있어서 주요한 이슈이다. 이에 본 논문에서는 사용자의 선호 취향과 대중의 선호취향을 모두 고려한 협업필터링 개념의 통계적 기계학습 기반 TV 프로그램 추천 모델을 제시한다. 이를 위해 시청한 TV 콘텐츠에 대한 선호 토픽을 사용자의 시청 선호도로 보고, 최근 널리 활용되고 있는 LDA(Latent Dirichlet Allocation)모델을 TV 프로그램 추천 모델에 적용하였다. LDA 기반 TV 프로그램 추천 성능을 개선하기 위해 본 논문에서는 TV시청 이용내역 데이터를 기반으로, TV 사용자들의 관심 토픽을 은닉 변수로 하고, TV 사용자들의 관심 토픽에 대한 다양성을 반영하기 위해 은닉 변수의 확률분포 특성을 비대칭 디리클레(Dirichlet) 분포로 모형화하여 실험에 적용하였다. 제안된 LDA 기반 TV 프로그램 자동 추천 방법의 성능을 검증하기 위해, 유사 시청 특성을 갖는 사용자 그룹에 대해 상위 5개의 TV 프로그램을 일주일 단위로 추천하였을 경우 평균 66.5%, 2개월 단위의 추천에 대해서는 평균 77.9%의 precision 추천 성능을 확인할 수 있었다. 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.

Online Scheduling of Targeted Advertisements for IPTV

Behavioral targeting of content to users is a huge and lucrative business, valued as a $20 billion industry that is growing rapidly. So far, the dominant players in this field like Google and Yahoo! examine the user requests coming to their servers and place appropriate ads based on the user's search keywords. Triple-play service providers have access to all the traffic generated by the users and can generate more comprehensive profiles of users based on their TV, broadband, and mobile usage. Using such multisource profile information, they can generate new revenue streams by smart targeting of ads to their users over multiple screens (computer, TV, and mobile handset). This paper proposes methods to place targeted ads to a TV based on user's interests. It proposes an ad auction model that can leverage multisource profile and can handle dynamic profile-based targeting like Google's AdWords vis-à-vis static demography-based targeting of legacy TV. We then present a 0.502-competitive revenue maximizing scheduling algorithm that chooses a set of ads in each time slot and assigns users to one of these selected ads.

Electronic Interests and Behaviours Associated with Gambling Problems

Multiple computing devices continue to develop capabilities that support online gambling, resulting in the need to evaluate the extent that this trend will contribute to gambling problems. A sample of 1,141 participants completed an online survey assessing interest in and difficulties limiting use of digital services. Questionnaire items measured a range of behaviours associated with electronic service use (e.g., surfing, complaints). Results indicated that individuals utilising digital services and experiencing problems limiting their TV and Mobile phone usage were more likely to report gambling problems. These respondents were more likely to click on gambling-related links, report receiving larger amounts of spam, make more attempts to unsubscribe, and to click on links to counselling sites. Exposure and interest in electronic modes of communication can be associated with a greater risk of gambling related problems.

An Automatic Recommendation Scheme of TV Program Contents for (IP)TV Personalization

Due to the rapid increase of contents available under the convergence of broadcasting and Internet, efficient access to personally preferred contents has become an important issue. In this paper, an automatic recommendation scheme based on collaborative filtering is presented for intelligent personalization of (IP)TV services. The proposed scheme does not require TV viewers (users) to make explicit ratings on their watched TV program contents. Instead, it implicitly infers the users' interests on the watched TV program contents. For the recommendation of user preferred TV program contents, our proposed recommendation scheme first clusters TV users into similar groups based on their preferences on the content genres from the user's watching history of TV program contents. For the personalized recommendation of TV program contents to an active user, a candidate set of preferred TV program contents is obtained via collaborative filtering for the group to which the active user belongs. The candidate TV programs for recommendation are then ranked by a proposed novel ranking model. Finally, a set of top- N ranked TV program contents is recommended to the active user. The experimental results show that the proposed TV program recommendation scheme yields about 77% of average precision accuracy and 0.135 value of ANMRR (Average Normalized Modified Retrieval Rank) with top five recommendations for 1,509 people.

Media Accessibility, Utilization and Preference for Food and Nutritional Information by Rural Women of India

The study investigated media utilization, preferences and prospect for use in nutrition education service delivery in three villages of Uttarakhand S tate. A total of 223 women respondents in the age group of 18-45 years were selected using a random sampling technique. Questionnaires/interview schedules were administered to them. Data collected were analyzed using frequency, mean, percentage, regression and weighted average. Media possession data showed that the TV was most commonly possessed and used media. Print media was found to be less popular compared to electronic media. Major reasons for TV usage/ preference were effectiveness for information dissemination, better understanding and entertainment. A regression analysis between respondents' personal and socio-economic characteristics and preference for media to obtain nutritional information indicated that age, income, marital status, and educational status determine media preference. The study revealed that media preference in descending order were: television, radio, poster , calendar, lecture, audiocassettes, booklets, charts and newspaper . It is, therefore, recommended that development agencies should take advantage of women's preferences to ensure optimum effect of various food and nutrition related extension programmes.

Hands-free voice communication with TV

This paper presents a system for full-duplex hands free voice communication integrated with TV technology. The system provides comfortable conversation by utilization of microphone array and advanced voice processing algorithms, even with simultaneous TV usage. Signal processing includes superdirective beamformer steered by direction-finding module, postprocessing module, acoustic echo canceller, stationary noise reduction module and automatic gain control. All processing is realized in real-time on DSP based platform. As communication channel GSM or VoIP can be used.

Performance Evaluation of Cellular Networks Offering TV Services

In this paper, the capacity of orthogonal frequency-division multiple-access (OFDMA) networks is evaluated when serving mobile TV traffic. To introduce the service within the network, we develop analytical cross-layer models to assess the performance of the system when carrying TV calls over unicast connections. The presented cross-layer analysis is general and can be adapted to all types of multimedia services. We then focus on a TV-specific capacity analysis, exploiting the possibility of broadcasting TV channels over point-to-multipoint (PtM) connections. This includes investigating an enhanced broadcast solution where the modulation is dynamically adapted to the radio conditions of connected TV users. The more complete broadcast solution over single-frequency networks (SFNs) is then evaluated, based on a novel physical-layer model integrating the combined signal gain from neighboring cells. We finally show how to maximize the system capacity through a secondary usage of the SFN resources by elastic traffic. The presented models are evaluated using numerical applications and further validated through network simulations.

The relationships among obesity, physical activity, the knowledge level about obesity, watching food ads on TV with interest, and the number of weekly school canteen usage in adolescents

This study investigates the effects of physical activity, the knowledge level about obesity, watching food ads on TV and school canteen usage on body mass index (BMI) for male (1227) and female (773) children in the middle and high schools. The results demonstrated that body height and weight, and BMI significantly differentiated with respect to age and gender groups. The mean body weight and BMI in exercise group were lower than sedanters. The mean body weight changed depending on knowledge level about obesity. The group watching food ads on TV has higher averages than sedanters in the body weight and BMI. School canteen usage has no effect on the body weight and BMI.

Integration of personal health systems into intelligent living environments

Solutions from information and communication technology (ICT) can contribute to the better care of chronic patients and the elderly. Personal health systems (PHS) are designed for remote health monitoring of home-residing patients (especially chronic patients), while intelligent living environments facilitate everyday tasks for the sick and disabled prolonging their independence and reducing the pressure on health institutions and nursing homes.The widespread prevalence of chronic diseases and the market potential of both PHS and intelligent homes indicate a potential user group for both types of systems. We believe that a tighter integration of the two types of systems could have beneficial results from both the technical implementation and cost of service, as well as from improved usability due to the lower number of different user devices and interfaces.Three of our pilot projects have integrated PHS in intelligent home systems. In the case of the IRIS Smart Home, we developed a system for monitoring blood pressure and integrated it into an existing solution for home environment control. We continued the work with the IDT project where we developed a comprehensive intelligent home solution designed for providing services within an operator's environment. ‘Moje zdravje’ (My health) was the third pilot project; here, we integrated health services into a next-generation TV user interface.The results of the pilot projects support the logic behind the suggested systems integration. The main benefits are the more effective technical implementation, improved usability, and lower cost of service. Further challenges lie in forming appropriate business models, creating adequate links to the health care information system, and proper inclusion of medical staff in the telehealth services.