Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Discovering TV contents in a second screen app: perspectives from Portuguese and Brazilian markets

The actual trends in the TV ecosystem present considerable social, organisational and technological challenges in the value-chain of Pay-TV operators. Among these is the global increase in bandwidth, the shifting of the storage capacity in the cloud, and the affordability of traditional content providers when facing the competitiveness of OTT content. In this framework, Pay-TV operators are presenting their customers with a huge offer of contents available from linear-TV, Catch-up TV and VoD services. However, this overloaded TV ecosystem is likely to lead viewers to get lost and face difficulties when deciding what to watch on TV. To overcome these difficulties and be competitive, operators need to provide innovative and trustable solutions, alternative to traditional EPGs, enabling users to discover the right content for a specific context. To target this problem, a second screen application (GUIDER) was developed to offer an original user interface, based on a multidimensional spatial representation of TV contents for those mindless zapping situations where viewers do not know, in advance, what they are in the mood to watch. This paper reports on the evaluation of the GUIDER App, aiming to determine the level of interest in the several features implemented and in the filtering criteria available; identify usability issues; and predicting the future uses of the App in domestic scenarios. The evaluation was made in Portugal and Brazil with a convenience sample of 20 participants in each country. Despite the differences in the TV ecosystems, both countries appear to be promising markets for this new kind of second screen applications, with Brazilians showing a higher perception of the added value of GUIDER

An architecture for evolving the electronic programme guide for online viewing

Watching television and video content is changing towards online viewing due to the proliferation of content providers and the prevalence of high speed broadband. This trend is coupled to an acceleration in the move to watching content using non-traditional viewing devices such as laptops, tablets and smart phones. This, in turn, poses a problem for the viewer in that it is becoming increasingly difficult to locate those programmes of interest across such a broad range of providers. In this thesis, an architecture of a generic cloud-based Electronic Programme Guide (EPG) system has been developed to meet this challenge. The key feature of this architecture is the way in which it can access content from all of the available online content providers and be personalized depending on the viewer’s preferences and interests, viewing device, internet connection speed and their social network interactions. Fundamental to its operation is the translation of programme metadata adopted by each provider into a unified format that is used within the core system. This approach ensures that the architecture is extensible, being able to accommodate any new online content provider through the addition of a small tailored search agent module. The EPG system takes the programme as its core focus and provides a single list of recommendations to each user regardless of their origins. A prototype has been developed in order to validate the proposed system and evaluate its operation. Results have been obtained through a series of user trials to assess the system’s effectiveness in being able to extract content from several sources and to produce a list of recommendations which match the user’s preferences and context. Results show that the EPG is able to offer users a single interface to online television and video content providers and that its integration with social networks ensures that the recommendation process is able to match or exceed the published results from comparable, but more constrained, systems

Deliverable D8.2 First market analysis

This deliverable provides an overview of a first market analysis of the IPTV market. It points out possible customers, competitors and the differences between LinkedTV and their competitive firms