Towards Next Generation Hybrid Broadcast Broadband, Results from FP7 and HBBTV 2.0

Deventer, M.O. van; Wit, J.J. de; Guelbahar, Mark; Cheng, Biyu; Mármol, Félix Gómez; Köbel, Christian; Köhnen, Christopher; Stockleben, Bjoern

doi:10.1049/ibc.2013.0049

Cited by 9 publications

(8 citation statements)

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“…In Hybrid Broadcast Broadband Television (HbbTV) project, they have designed several frameworks for identifying a viewer in front of a smart TV that include multi-user identification and multi-user recommendations [17,18,31]. HbbTV is heavily criticized in the literature for its security and privacy concerns as it is capable of capturing private data, such as picture and profile information [19].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The explicit feedbacks are difficult to provide in a smart TV environment [47]. Therefore, in a typical smart TV watching scenario, implicit feedbacks may be a better option than explicit feedbacks [18]. The second approach used by the recommender system is content-based filtering technique, which rely on the user's profile and item profile (description).…”

Section: Limitations Of Recommendation Approachesmentioning

confidence: 99%

“…Moreover, we cannot expect the same interactions from viewers as they normally have with computers and smartphones [17]. In addition to the interactive nature of smart TV, it is enjoyed as a lean-back device and passive as legacy TV systems [18].…”

Section: Introductionmentioning

confidence: 99%

“…The approaches for analyzing user activities, such as data mining, clickstream analysis, and in-depth identification of a viewer(s) may lead to serious security and privacy threats [18]. Although security and privacy issues for smart TV are in the infancy stage, they are the most important concerns for smart TV viewers [19].…”

Section: Introductionmentioning

confidence: 99%

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Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

2019

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The recommender systems are deployed on the Web for reducing cognitive overload. It uses different parameters, such as profile information, feedbacks, history, etc., as input and recommends items to a user or group of users. Such parameters are easy to predict and calculate for a single user on a personalized device, such as a personal computer or smartphone. However, watching the Web contents on a smart TV is significantly different from other connected devices. For example, the smart TV is a multi-user, lean-back supported device, and normally enjoyed in groups. Moreover, the performance of a recommender system is questionable due to the dynamic interests of groups in front of a smart TV. This paper discussed in detail the existing recommender system approaches in the context of smart TV environment. Moreover, it highlights the issues and challenges in existing recommendations for smart TV viewer(s) and presents some research opportunities to cope with these issues. The paper further reports some overlooked factors that affect the recommendation process on a smart TV. A subjective study of viewers’ watching behavior on a smart TV is also presented for validating these factors. Results show that apart from all technological advancement, the viewers are enjoying smart TV as a passive, lean-back device, and mostly used for watching live channels and videos on the big screen. Furthermore, in most households, smart TV is enjoyed in groups as a shared device which creates hurdles in personalized recommendations. This is because predicting the group members and satisfying each member is still an issue. The findings of this study suggest that for precise and relevant recommendations on smart TVs, the recommender systems need to adapt to the varying watching behavior of viewer(s).

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Section: Literature Reviewmentioning

confidence: 99%

Section: Limitations Of Recommendation Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

2019

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“…• Coherence and synchronization -the same content is shown on different screens of the user (advanced synchronisation of content is discussed eg in [20]). This area can be extended by idea of co-watching and controlling the same content from different locations (eg friends share "virtual living room" they watch the same video from different locations and play/pause it anytime).…”

Section: Second Screen Utilizationmentioning

confidence: 99%

Enhanced hybrid TV platform with multiscreen, advanced EPG and recommendation enablers

Kováčik

Bencel

Mato

et al. 2017

Journal of Electrical Engineering

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TV watching dramatically changes with introduction of new technologies such as Internet-connected TVs, enriched digital broadcasting (DVB), on-demand content, additional programme information, mobile phones and tablets enabling multiscreen functions etc that offer added values to content consumers. In this paper we propose modular advanced TV platform and its enablers enhancing TV watching. They allow users to receive aside of EPG also additional information about broadcasted content, to be reminded of requested programme, to utilize recommendation and search features, thanks to multiscreen functionality to allow users to take watched content with them or transfer it onto another device. The modularity of the platform allows new features to be added in future.

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Interactive TV Interoperability and Coexistence: The GLOBAL ITV Project

Angeluci

Calixto

Morandini

et al. 2015

Communications in Computer and Information Science

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Towards Next Generation Hybrid Broadcast Broadband, Results from FP7 and HBBTV 2.0

Cited by 9 publications

References 0 publications

Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

Enhanced hybrid TV platform with multiscreen, advanced EPG and recommendation enablers

Interactive TV Interoperability and Coexistence: The GLOBAL ITV Project

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