Making the most of TV on the move: My newschannel☆

Pazos‐Arias, José J.; Vilas, Ana Fernández; Redondo, Rebeca P. Díaz; Gil‐Solla, Alberto; Ramos‐Cabrer, Manuel; Duque, Jorge García

doi:10.1016/j.ins.2010.10.017

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Cited by 10 publications

(2 citation statements)

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“…The initial user profile can be obtained with various techniques: extracting information about user interests from a set of training data or questionnaires [25,30], classifying users into a particular group of users and assigning them a previously created stereotyped profile [1], or allowing a manual specification of the initial profile [23,30,34].…”

Section: Related Workmentioning

confidence: 99%

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On-line dynamic adaptation of fuzzy preferences

Marin

Isern

Moreno

et al. 2013

Information Sciences

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Section: Related Workmentioning

confidence: 99%

“…Recently, researchers have used semantic information, provided by specific domain ontologies, during the personalization and adaptation of the user's interests [1,7,11,12,15,24]. These processes usually annotate the resources and link the data to the user profile.…”

Section: Related Workmentioning

confidence: 99%

On-line dynamic adaptation of fuzzy preferences

Marin

Isern

Moreno

et al. 2013

Information Sciences

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Experiences in Using Semantic Technologies for Providing Added‐Value Services in DigitalTV

Vilas

Redondo

Pazos‐Arias

et al. 2015

Handbook of Digital Imaging

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Interactive digital TV ( IDTV ) has changed the traditional perception people used to have about audiovisual content consumption. The merely passive viewers are adopting active roles by interacting with the applications that are broadcast over the IDTV networks. Besides, the great amount of broadcast information (audiovisual and applications) opens new possibilities for the audience, who has a broader bouquet of content to explore. However, and overwhelmed by the filtering process, users need a service to help them to decide what to watch. At this point, we propose to apply semantic tecniques to improve the filtering tasks and so, the experiences described in this chapter have the semantic TV content recommender that we have developed, AVATAR , as base for offering new serviced to the TV audience: advertising techniques, t‐learning, and adopting special solutions for the TV on the move.

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Realtime Channel Recommendation: Switch Smartly While Watching TV

Zhao

Zhou

et al. 2016

Frontiers in Algorithmics

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Making the most of TV on the move: My newschannel☆

Cited by 10 publications

References 27 publications

On-line dynamic adaptation of fuzzy preferences

On-line dynamic adaptation of fuzzy preferences

Experiences in Using Semantic Technologies for Providing Added‐Value Services in DigitalTV

Realtime Channel Recommendation: Switch Smartly While Watching TV

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