Smart TV Services Research Articles

Identification of usability problems and requirements of elderly Chinese users for smart TV interactions

ABSTRACTWith the development of Information and Communications Technology (ICT), smart TV is gradually becoming universal and penetrating daily life. Smart TV has a wide range of user groups, and the elderly is an important group. The special physiological and psychological characteristics of Chinese elderly highlight the usability problems of smart TV interactions for them. The main functions of smart TVs were selected and operated by the elderly in this study. With the help of physiological measurements, behaviour analyses and interviews during natural usage scenarios, we determined the usability issues and user requirements of Chinese elderly for interacting with smart TVs. The research shows that the elderly in China have the intention to use smart TV products, but the usability of interactive systems affects the experience of using a smart TV. The results indicate that different content search methods result in different user experiences. Pinyin search is difficult to operate using a remote control, and some influencing factors are revealed. Voice search results in the best user experience for the elderly, but the recognition accuracy is easily affected by factors such as user accents and environmental noise. Hierarchical search is easy to operate, but it often takes a long time to finish an involved task. Other usability issues of some main functions, i.e. screen mirroring, shopping, playing games, system settings and application downloads, were also obtained, and the requirements of the elderly were well understood. These findings have implications for interaction design and implementation of smart TV service systems.

The Changing Face of the German Broadcast Television Industry

This study offers an economic analysis of the television broadcasting industry at the convergence of broadcast and broadband connectivity. It applies the „Structure-Conduct- Performance“(SCP)-paradigm of Industrial Organization theory as framework and investigates how the model can contribute to explaining emergent issues in the economics of Smart TV. It is argued here that SCP model provides a useful heuristic and can fruitfully be applied to various questions surrounding the fundamental transformation of the television broadcasting sector towards Smart TV, notably issues of market structure and competitive behavior. The argument is illustrated by the emerging ecosystem for Smart TV services in Germany. There, the market is still immature and each provider is struggling with the challenge of developing an optimal strategy and organizational form in the Smart TV ecosystem.

The Changing Face of the German Broadcast Television Industry

This study offers an economic analysis of the television broadcasting industry at the convergence of broadcast and broadband connectivity. It applies the „Structure-Conduct- Performance“(SCP)-paradigm of Industrial Organization theory as framework and investigates how the model can contribute to explaining emergent issues in the economics of Smart TV. It is argued here that SCP model provides a useful heuristic and can fruitfully be applied to various questions surrounding the fundamental transformation of the television broadcasting sector towards Smart TV, notably issues of market structure and competitive behavior. The argument is illustrated by the emerging ecosystem for Smart TV services in Germany. There, the market is still immature and each provider is struggling with the challenge of developing an optimal strategy and organizational form in the Smart TV ecosystem.

Factors influencing the low usage of smart TV services by the terminal buyers in Korea

Smart TV in Korea faces an asymmetric situation between the purchase of terminals and the usage of services. In spite of this low utilization, there are few prior studies on the causes of adoption barriers for smart TV services in Korea. This study focuses on the research problem of, “Why do people not use smart TV services even after purchasing smart TV terminal in Korea?” Using survey data collected from Korean buyers of smart TV terminals, this study empirically analyzes the factors influencing the low usage of smart TV services. The study’s results show that a lack of sub-services, low network quality, and low usability significantly affect the usefulness of smart TV services. Further, the low usefulness, low usability, and alternatives are the primary determinants of low intention of using smart TV services.

Germany ∙ German Data Protection Authorities Issue Privacy Guidelines for Smart TV Services

Germany ∙ German Data Protection Authorities Issue Privacy Guidelines for Smart TV Services

Trace select: acquiring remote targets with gesture guides generated from glyphs

When selecting targets in a Smart TV service, the user is frequently required to select remote targets such as menu items, but is unable to touch the targets directly or use a mouse. Solutions include rapid selection techniques using gesture guides. However, such techniques create difficulties, in that they tend to hide the original information. This study proposes a novel technique for selecting remote targets, referred to as Trace Select, which dynamically generates gesture guides from text target characters. The guides do not conceal the original targets. As a result, the guides can be continuously displayed. The technique does not require immediate activation of the gesture-guide displays, and users can identify and draw the gesture of the desired target at any time. Experiments show that Trace Select outperforms both cursor-based selection enhanced by acceleration and conventional activation-required gestural selection.

A Subjectivity Study on the Promotion of Korean Smart TV Industry through Q Methodology

From the perspectives of regulation and convergence, several debates on the industrial definition of smart TV service in the Korean broadcasting and communications market are currently in progress. The most heated controversy mainly depends on whether smart TV is controlled under broadcasting regulations or under communications regulations. This Korean- specific problem is summarized in regulation fairness or asymmetric regulations between smart TV and IPTV operators. Although both operators utilize very similar technology and share the same business model, the regulations for only smart TV operators are unfettered. This would be an intrinsic cause that prohibits fair competition in the new converging market. Currently we confront the matter of asymmetric regulations between smart TV and others including IPTV or ISP. The purpose of this study, thus, is to provide the strategic, regulatory guidance and theoretical definitions on smart TV service, as one of converging services, based on a qualitative approach through subjectivity methodology. The study identifies three issues net neutrality, regulation fairness and new media and new market and proposes desirable promotion plans regarding those issues. The study draws lessons learned through a Korean smart TV case and provides directions to strategic-policy studies for future emergent converging services.

Video Content-Based Bit Rate Estimation Scheme for Transcoding in IPTV Services

In this paper, a new bit rate estimation scheme is proposed to determine the bit rate for each subclass in an MPEG-2 TS to H.264/AVC transcoder after dividing an input MPEG-2 TS sequence into several subclasses. Video format transcoding in conventional IPTV and Smart TV services is a time-consuming process since the input sequence should be fully transcoded several times with different bit-rates to decide the bit-rate suitable for a service. The proposed scheme can automatically decide the bit-rate for the transcoded video sequence in those services which can be stored on a video streaming server as small as possible without losing any subject quality loss. In the proposed scheme, an input sequence to the transcoder is sub-classified by hierarchical clustering using a parameter value extracted from each frame. The candidate frames of each subclass are used to estimate the bit rate using a statistical analysis and a mathematical model. Experimental results show that the proposed scheme reduces the bit rate by, on an average approximately 52% in low-complexity video and 6% in high-complexity video with negligible degradation in subjective quality.

온톨로지 기반 영화 메타데이터간 연관성을 활용한 영화 추천 기법

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and protg API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

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.

MAKING DIGITAL TV SMARTER: CAPTURING AND REUSING EXPERIENCE IN DIGITAL TV

In this article, we explore an approach that integrates decisional DNA, a domain-independent, flexible, and standard knowledge repository, with digital TV in order to capture, reuse, and share viewers’ TV watching experience and preferences. Key issues in applying this approach include capturing of experience, storage and management of experience, and retrieval of experience from experience repository. We demonstrate our approach in a set of initial experiments, in which viewers’ movie watching experiences are captured and reused to support smart digital TV services.

시뮬레이션을 이용한 대규모 스마트 TV 서비스 제공을 위한 사용자 그룹핑 알고리즘 성능 분석

ABSTRACTSmart TV System will lead to drastic change of communication and media industries as one of the emerging next generation network services. However, when the number of concurrent users increases rapidly, the issue of service quality degradation occurs because providing services to many users simultaneously stresses both the server and the network. The server limitation can be circumvented by deploying server clusters. but the network limitation is far less easy to cope with, due to the difficulty in determining the cause and location of congestion and in provisioning extra resources. In order to alleviate these problems, a number of schemes have been developed. Prior works mostly focus on reducing user-centric performance metrics of individual connection, such as the round-trip time(RTT), downloading time or packet loss rate, but tend to ignore the network loads caused by the concurrent connections or global network load balance. In this work, we make an in-depth investigation on the issue of user grouping for massive Smart TV services through simulations on actual Internet test-bed, PlanetLab.