Bernardo Cardoso

Catch-up TV analytics: statistical characterization and consumption patterns identification on a production service

Multimedia IP Television services, such as on-demand Catch-up TV, are in an active migration process towards Over-The-Top (OTT) delivery using state-of-the-art Content Delivery Networks (CDNs). Maintaining the same Quality-of-Experience (QoE) of managed IPTV networks is challenging and requires a thorough understanding of users’ behaviors and content demand characteristics. This article leverages Catch-up TV usage logs obtained from a Pay-TV operator’s live production IPTV service containing over 1 million subscribers to characterize and extract insights from service utilization at a scale and scope not yet addressed in the literature. A detailed analysis on the characteristics of users’ viewings is performed, with a study of when, where, and how often users access the service, along with how they behave during each viewing session. The results show that Catch-up TV consumption exhibits very high levels of utilization throughout the day, and is heavily polarized towards specific genres, recently aired programs, and content broadcasted during prime-time. The superstar effect is notorious. This analysis is complemented by a service optimization perspective, which shows that large gains are achievable by caching popular programs, and by loading content in advance to users’ Set-Top-Boxes (STBs). This comprehensive research study is supplemented by detailed statistical information tables, which highlight the feasibility of efficiently migrating Catch-up TV services to OTT-scenarios, and provide the foundations for future works able to explore these results.

Voice Interaction on TV: Analysis of natural language interaction models

The goal of this study is to perform an analysis of natural language models of interaction, associated to 3 types of input devices with a microphone, a hands-free device, a mobile app and a remote control, in order to identify the most suited to be adopted in a conversational voice solution for interactive television in Portuguese. The research addressed issues associated with natural language systems such as usability, interaction and privacy, using a prototype based on a Wizard-of-Oz methodology. It was possible to verify that the interaction model that met the majority of preferences was the hands-free solution, based on a far-field microphone activated by a wake-up word. Aside from easy to use, it was the one that raised the least difficulties of execution. Despite this, the interaction model based on the remote control with microphone was not completely set aside. Some participants pointed out that the remote control, as a device inherently associated with television, is the most natural way of interaction. Although most participants indicate that a natural language interaction system does not raise significant privacy issues, it is important to have this aspect in consideration when choosing the interaction model to be adopted on a voice system commercial solution. To top it off, participants consider that a voice-operated system, like the one they were evaluating, is very useful and almost all have been receptive to having such a system at home.

Catch-up TV forecasting: enabling next-generation over-the-top multimedia TV services

Due to recent developments in Over-The-Top (OTT) technologies, Pay-TV operators have begun a migration process of managed IP Television (IPTV) services to more appealing OTT approaches. In these scenarios, being able to predict when and what resources will be necessary at any given point is crucial to a high-quality, efficient, and cost-effective operation, especially when dealing with the dynamic and resource-intensive requirements of IPTV multimedia services. To evaluate the advantages of demand forecasting for efficient Catch-up TV delivery on OTT scenarios, this research work explores several classes of machine learning models regarding their accuracy, computational requirement trade-offs, and deployability. The training process relies on a dataset comprised of Catch-up TV usage logs acquired from an IPTV operator’s live production service containing over 1 million subscribers. A predictive and dynamic resource provisioning approach is proposed and evaluated in terms of bandwidth and storage savings. Results demonstrate that forecasting Catch-up TV demand is practical, suitable for integration in OTT solutions, and useful in improving efficiency, with benefits to operators and consumers. Significant savings in bandwidth and storage are shown to be achievable, enabling green and cost-effective resource usage.

Machine Learning the TV Consumption: A Basis for a Recommendation System

With the continuous growth of channels and content available in a typical interactive TV service, viewers have become increasingly frustrated, struggling to select which programs to watch. Content recommendation systems have been pointed out as a possible tool to mitigate this problem, especially when applied to on-demand content. However, in linear content, its success has been limited, either due to the specificities of this type of content or due to the little integration with normal consumption behaviors. Despite that, recommendation algorithms have undergone a set of enhancements in order to improve their effectiveness, particularly when applied to the world of linear content. These improvements, focused on the use of the visualization context, paired with machine learning techniques, can represent a big advantage in the quality of the suggestions to be proposed to the viewer. The area of user experience (UX) evaluation, in interactive TV, has been also a subject of ongoing research, extending beyond the traditional usability evaluation, pursuing other dimensions of analysis such as identification, emotion, stimulation, and aesthetics, as well as distinct moments of evaluation. This paper presents the proposal for the development of a recommendation system, based on the viewing context, and a methodology for evaluating the way this system influences the UX of the viewers.

Indagante: A Proposal for a Social Multiplatform Game to Motivate Interaction in the Living Room

Nowadays, the access to new personal and portable digital equipment capable to reproduce all kinds of media content is getting increasingly easy. However, the presence of these new devices in the living room resulted in family members sharing the same physical space but not talking to each other. In this article, we intend to highlight these observations, as well to present a proposal based on a digital game, associated with television content, which has the potential to create an effect of discussion and conversation between those present in the living room. We will also highlight the way this game and its dynamics were designed, with the Interaction Design process in mind, to allow the game to reach its full potential: be fun and breed discussion.