Evaluation of Virtual Keyboards for Interactive Digital Television Applications

The development of the technologies behind Interactive Digital Television (IDTV) services has produced a new type of audience. Traditional viewers now become users as they may play an active role in front of the TV, for example, by choosing a video to be played on demand or by introducing text on an IDTV application. In these services, interactions need to be performed with a remote control, currently the main interaction device, or other devices such as keyboards or mice, which are not very popular in this environment. Nevertheless, although remote controls are essential tools for IDTV services, they are very limited when it comes to writing text. Thus, this study evaluated different alternatives to introduce text on an IDTV application with a remote control. A heterogeneous group of people was selected to write predefined sentences in Spanish in a test environment using three virtual keyboard layouts and the multitap mechanism. Their performance and subjective impressions reveal weaknesses and strengths of the evaluated methods. The article draws important conclusions about the usage of remote controls in IDTV applications, not only for the design of new applications but also for the research of new techniques to introduce text.     

Making the most of TV on the move: My newschannel

The increasing success of mobile-TV (m-TV) is changing the habits and customs in TV consumption which now extends to parts of the day when viewers are not at home and, mainly, within short intervals between other daily activities. Since much m-TV consumption will be in spare time, offering the users contents they are interested in becomes extremely important to provide an attractive service which does not discourage potential users. Using DVB-H as transport infrastructure, in this paper we introduce a personalization architecture which perfectly fits in with the value chain of m-TV. By applying semantic models and techniques, personalized virtual channels are dynamically constructed by adequately combining several kinds of contents according to the user’s interests. While the personalization architecture is particularized, in this paper, to provide news channels, it could be easily generalized to other fields.     

A new territory of multi-user variable remote control for interactive TV

Unlike passive analog TV, interactive TV (iTV) is the next step toward interactivity that offers users a friendly interactive experience. As there are fewer studies focused on multi-user interaction in a local environment, we concentrate on multi-user interaction with families, especially within a home information system. Therefore, we propose a control service framework “MVC-iTV,” based on distributed computing and Machine-to-Machine (M2M) communications, in which external displays (TVs or projectors) and handheld devices can be used for controlling iTV services by several members of the family at the same time, without any relocation. Further, the new remote control framework can free users from the restrictions of using one remote controller. The experiment results indicate that the MVC-iTV framework is applicable to the iTV service and that users can operate the service in any visible environment via wireless networking technologies. Besides, requirements of the hardware compatibility of the TV appliance and remote controller will be reduced in the MVC-iTV framework.     

Social experiences within the home using second screen TV applications

Today, using second screen devices while watching TV is quite common, whether related to what happens on TV or not. One area of research looks at using second screen devices to support social interaction. While most research in this area focuses on supporting social interaction between remote viewers, in this article, we focus on social interaction between collocated viewers, using second screen applications that were designed for a specific TV program. We present the results of five studies that were carried out in three different phases of a user-centered design cycle (analysis, design and evaluation) and report on the social interaction that occurs when groups of viewers use such applications in the home and on the factors that have an influence on this social experience. Based on these findings we formulate a number of guidelines for the design of social second screen applications. We found that most participants valued such applications because of the increased interactivity and the social experience. Furthermore, applications that incorporate some form of competition are especially compelling. However, care needs to be taken when introducing competitive elements into an application and when choosing a suitable TV genre.

     

Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study

Current recommender systems attempt to identify appealing items for a user by applying syntactic matching techniques, which suffer from significant limitations that reduce the quality of the offered suggestions. To overcome this drawback, we have developed a domain-independent personalization strategy that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relationships inferred between the user’s preferences and the available items. Our reasoning-based approach improves the quality of the suggestions offered by the current personalization approaches, and greatly reduces their most severe limitations. To validate these claims, we have carried out a case study in the Digital TV field, in which our strategy selects TV programs interesting for the viewers from among the myriad of contents available in the digital streams. Our experimental evaluation compares the traditional approaches with our proposal in terms of both the number of TV programs suggested, and the users’ perception of the recommendations. Finally, we discuss concerns related to computational feasibility and scalability of our approach.     

A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition

With the advent of new cable and satellite services, and the next generation of digital TV systems, people are faced with an unprecedented level of program choice. This often means that viewers receive much more information than they can actually manage, which may lead them to believe that they are missing programs that could likely interest them. In this context, TV program recommendation systems allow us to cope with this problem by automatically matching user’s likes to TV programs and recommending the ones with higher user preference.This paper describes the design, development, and startup of queveo.tv: a Web 2.0 TV program recommendation system. The proposed hybrid approach (which combines content-filtering techniques with those based on collaborative filtering) also provides all typical advantages of any social network, such as supporting communication among users as well as allowing users to add and tag contents, rate and comment the items, etc. To eliminate the most serious limitations of collaborative filtering, we have resorted to a well-known matrix factorization technique in the implementation of the item-based collaborative filtering algorithm, which has shown a good behavior in the TV domain. Every step in the development of this application was taken keeping always in mind the main goal: to simplify as much as possible the user task of selecting what program to watch on TV.     

Data transmission schemes for DVD-like interactive TV

Current interactive services for digital TV are limited. They basically display a Web page alongside the TV program, which enhances the viewer's experience by providing extra information about the TV program. We define new interactive services for digital TV, which provide DVD-like interactivity to TV viewers. These services enable viewers to control the content and final presentation of a TV program. Some of the attractive applications of our services include parental management, multilingual audio, multiangle video, video in video, etc. The challenge in implementing these services is in transmitting an extra audio or video stream (called incidental) along with the main streams of the TV program. In the first part of this paper, we present a framework for adding the incidental streams to the original transmission stream without increasing the required bandwidth, degrading the picture quality of the main streams, or violating the compatibility of the transmitted stream with standard TV receivers. In the second part of this paper, we explore the two basic mechanisms of the presented framework: traffic characterization and admission control. We present methods for implementing these mechanisms. Using our methods, one can determine whether a TV transmission network has the capability of sending an incidental stream or not. Simulations were conducted to test the validity of our method. The results verify that our method successfully transmits the incidental streams without any discrepancy and without affecting the quality of the main streams.     

一种面向多用户的电视节目推荐生成方法

针对越来越丰富的电视节目资源和多用户同时观看电视的现实,文章提出了一种面向多用户的电视节目推荐生成方法,描述并设计了实现该方法的关键技术:用户对节目喜好程度度量,用户时间优先级度量,以及多个单用户节目单的融合算法。     

Users’ needs, desires, and design preferences for recommendations in the living room

By using a combination of playful probing, creative cultural probing and technology probing, user needs and desires for recommendations in the living room were investigated. 40 households with a total of 126 participants took part in the ethnographic study. The participants used the probing material, received recommendations from an interactive TV (iTV) recommender system, and logged their TV watching behaviour. The findings show that recommendations should preferably be given within the same media (the iTV system), that users prefer to influence the proposal that they receive rather than getting automated recommendations and that the design should ideally support recommendations for the whole household and additionally for individuals. The study revealed that in order to understand which factors account for a successful recommendation system from the user’s perspective, especially trust and security have to be further investigated.     

Towards dynamic behavior-based profiling for reducing spatial information overload in map browsing activity

The quantity of available detailed spatial content over the Web is continually growing. This leads to the problems of information overload and lengthy map download and render times. In order to address these problems in an effective and unobtrusive manner, the available content must be implicitly filtered and prioritized according to the user’s interests. Personalization of the user’s map elides less relevant information and prioritizes relevant information. The authors previously introduced a novel technique for detailed behavior-based spatial profiling. This article provides an analysis of the technique while exploring the properties of user interactions with a typical Web-based map browsing system. A technique for the automatic identification of specific interaction patterns is introduced and explored in a bid to improve current behavior-based map personalization techniques. The goal of this work is to move towards real-time profiling to support spatial dataset personalization, thus improving the user experience by reducing information overload.     

TV Program Recommendation for Multiple Viewers Based on user Profile Merging

Since today’s television can receive more and more programs, and televisions are often viewed by groups of people, such as a family or a student dormitory, this paper proposes a TV program recommendation strategy for multiple viewers based on user profile merging. This paper first introduces three alternative strategies to achieve program recommendation for multiple television viewers, discusses, and analyzes their advantages and disadvantages respectively, and then chooses the strategy based on user profile merging as our solution. The selected strategy first merges all user profiles to construct a common user profile, and then uses a recommendation approach to generate a common program recommendation list for the group according to the merged user profile. This paper then describes in detail the user profile merging scheme, the key technology of the strategy, which is based on total distance minimization. The evaluation results proved that the merging result can appropriately reflect the preferences of the majority of members within the group, and the proposed recommendation strategy is effective for multiple viewers watching TV together.     

User-selectable interactive recommendation system in mobile environment

Mobile devices need to provide more accurate and personalized information in a computing environment with a small screen and limited information retrieval functions. This paper presents a user-selectable recommendation system that reflects a user interest group by employing collaborative filtering as technique to provide useful information in a mobile environment. We form similar groups by simultaneously considering a user’s information preferences and demographics. Then we reconstruct lists of a final recommendation based on what search results the similar demographic group has chosen. This is an optional filter for the search results. This means that we provide an interactive flexible recommendation list that considers a user’s intent more actively, rather than unilaterally. We show the Mean Absolute Error result to evaluate the recommendation and finally show the realization of a prototype that is based on both the iPhone and Android phone environments.     

FrameIDTV: A framework for developing interactive applications on digital television environments

Digital TV technologies are stretching out the horizons for interactive applications to end users. However, developing interactive applications for this new environment is not as straightforward as developing Internet applications. In order to make this development process easier in the context of interactive digital TV (iDTV), one of the solutions is the development of Frameworks. However, after investigating some of the existing iDTV framework solutions, we have found a limitation on the range of the interactive application domain to DTV available in these current frameworks, which mainly show solutions for local interactive (enhanced TV). Thus, in this paper, we propose an iDTV Framework (called FrameIDTV) that makes possible a broader solution in the DTV domain for the construction of interactive applications, both local and remote. The remote interactivity is possible using a generic and easy to customize communication protocol that is specified in the framework for the application layer. Furthermore, applications such as voting, home banking, and t-commerce, which are executed locally in the set-top box and are integrated to remote services using a return channel, can use the proposed framework. FrameIDTV also allows the establishment of secure communications and is developed following a specific framework methodology. This framework has primarily targeted the Brazilian Digital TV standard and its procedural middleware, called Ginga-J. Nevertheless, FrameIDTV can be broadly used worldwide, since it is compliant with the Globally Executable MHP (GEM) standard.     

Conversational Collaborative Recommendation – An Experimental Analysis

Traditionally, collaborative recommender systems have been based on a single-shot model of recommendation where a single set of recommendations is generated based on a user’s (past) stored preferences. However, content-based recommender system research has begun to look towards more conversational models of recommendation, where the user is actively engaged in directing search at recommendation time. Such interactions can range from high-level dialogues with the user, possibly in natural language, to more simple interactions where the user is, for example, asked to indicate a preference for one of k suggested items. Importantly, the feedback attained from these interactions can help to differentiate between the user’s long-term stored preferences, and her current (short-term) requirements, which may be quite different. We argue that such interactions can also be beneficial to collaborative recommendation and provide experimental evidence to support this claim.     

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Recommender systems fight information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, using syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommending only items that bear strong resemblance to those the user already knows. Traditional collaborative approaches face up to overspecialization by considering the preferences of other users, which causes other severe limitations. In this paper, we avoid the intrinsic pitfalls of collaborative solutions and diversify the recommendations by reasoning about the semantics of the user’s preferences. Specifically, we present a novel content-based recommendation strategy that resorts to semantic reasoning mechanisms adopted in the Semantic Web, such as Spreading Activation techniques and semantic associations. We have adopted these mechanisms to fulfill the personalization requirements of recommender systems, enabling to discover extra knowledge about the user’s preferences and leading to more accurate and diverse suggestions. Our approach is generic enough to be used in a wide variety of domains and recommender systems. The proposal has been preliminary evaluated by statistics-driven tests involving real users in the recommendation of Digital TV contents. The results reveal the users’ satisfaction regarding the accuracy and diversity of the reasoning-driven content-based recommendations.     

A recommender system for the TV on the web: integrating unrated reviews and movie ratings

The activity of Social-TV viewers has grown considerably in the last few years—viewers are no longer passive elements. The Web has socially empowered the viewers in many new different ways, for example, viewers can now rate TV programs, comment them, and suggest TV shows to friends through Web sites. Some innovations have been exploring these new activities of viewers but we are still far from realizing the full potential of this new setting. For example, social interactions on the Web, such as comments and ratings in online forums, create valuable feedback about the targeted TV entertainment shows. In this paper, we address this last setting: a media recommendation algorithm that suggests recommendations based on users’ ratings and unrated comments. In contrast to similar approaches that are only ratings-based, we propose the inclusion of sentiment knowledge in recommendations. This approach computes new media recommendations by merging media ratings and comments written by users about specific entertainment shows. This contrasts with existing recommendation methods that explore ratings and metadata but do not analyze what users have to say about particular media programs. In this paper, we argue that text comments are excellent indicators of user satisfaction. Sentiment analysis algorithms offer an analysis of the users’ preferences in which the comments may not be associated with an explicit rating. Thus, this analysis will also have an impact on the popularity of a given media show. Thus, the recommendation algorithm—based on matrix factorization by Singular Value Decomposition—will consider both explicit ratings and the output of sentiment analysis algorithms to compute new recommendations. The implemented recommendation framework can be integrated on a Web TV system where users can view and comment entertainment media from a video-on-demand service. The recommendation framework was evaluated on two datasets from IMDb with 53,112 reviews (50 % unrated) and Amazon entertainment media with 698,210 reviews (26 % unrated). Recommendation results with ratings and the inferred preferences—based on the sentiment analysis algorithms—exhibited an improvement over the ratings only based recommendations. This result illustrates the potential of sentiment analysis of user comments in recommendation systems.     

Twitter analysis algorithms for Intelligence Circulation System

The convergence of broadcasting and broadband communications network technologies has attracted increasing attention as a means to enrich the television viewing experience of viewers. Toward this end, this study proposes the ‘Intelligence Circulation System (ICS)’, which provides several services, by using newly developed algorithms for analysing Twitter messages. Twitter users often post messages about on-air TV programmes. ICS obtains viewer responses from tweets without requiring any new infrastructure or changes in users’ habits or behaviours, and it generates and provides several outputs to heterogeneous devices based on the analysis results. The algorithms—designed by considering the characteristics of Twitter messages about TV programmes—use auxiliary programme information, similarity between messages, and time series of messages. An evaluation of our algorithms using Twitter messages about all programme genres for a month showed that the accuracy of topic extraction was 85 % for an emphasis on quality (with 56 % of messages processed) and 65 % for an emphasis on quantity (with 95 % of messages processed). The accuracy of message sentimental classification was 66 %. We also describe social recommendation services using the analysis result. We have created a Social TV site for a large-scale field trial, and we have analysed users’ behaviours by comparing four types of social recommendation services on it. The experimental result shows that active and passive communication users had different needs with regard to the recommendations. ICS can generate recommendations for satisfying the needs of both user types by using the analysis result of Twitter messages.     

A cloud-based intelligent TV program recommendation system

In recent years, cloud computing technology has matured significantly, as has the development of digital TV services. This, therefore, has led to an increased demand for improved quality TV services. In this paper, cloud computing technology is used to build a program recommendation system for digital TV programs, and the Hadoop Fair Scheduler is utilized to improve processing performance. Historical data of watched TV programs are collected through an electronic program guide, and then processed using K-means clustering, term frequency/inverse document frequency and k-nearest neighbor algorithms, to obtain clusters of audience groups and to find popular TV programs for each cluster. The proposed system can process massive amounts of user data in real-time, and can easily be scaled up.     

Automatic provision of personalized e-commerce services in Digital TV scenarios with impermanent connectivity

Digital TV (DTV) bears much potential for e-commerce, but most of it remains underexploited due to the lack of systematic approaches to the provision of interactive applications. Working on top of an existing recommender system, this paper presents mechanisms to automatically compose applications that provide personalized commercial functionalities to the users. The proposal addresses specific concerns of DTV environments, with special attention to achieving good personalization quality and furnishing useful interactions when there is no return channel permanently available for bidirectional communication. Preliminary experiments reveal good acceptance of the concept among real users.