What Can I Watch on TV Tonight?

Bueno, David; Conejo, Ricardo; Martin, David A.; León, Jorge G. Mendoza; Recuenco, Javier

doi:10.1007/978-3-540-70987-9_32

Cited by 3 publications

(6 citation statements)

References 4 publications

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“…We kept the size of the active item's neighborhood fixed to 60 (as in [55]), and ran our algorithm repeatedly for different values of k, k = {2, 4,6,8,10,11,12,13,14,15,16,17,18,19,20,22,24,26,28,30,35,40,45, 50}, which is a bigger set of values than in [55] where authors used only eight values of k. Fig. 10 collects the mean absolute errors observed by those runs, while the averaged over the five data splits from the data set is shown in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, we also highlight the work in [11] where another hybrid method is shown, which combines content-based recommendation and folksonomies (collaborative and social recommendations). They use as a collaborative recommender a group of algorithms called Slope One [27], which is possibly the simplest method of non-trivial item-based collaborative filtering based on points, but they provide no solutions neither to the sparsity nor the scalability problems of CF algorithms.…”

Section: Previous Research In Tv Program Recommendation Systemsmentioning

confidence: 98%

“…8). Her ratings history is constantly updated although the most recent evaluations are not considered to be more important than the older ones, as in [11], because we do not believe her taste changes rapidly.…”

Section: Ratings Gatheringmentioning

confidence: 99%

“…More recent works in this area are [61,22,53,11]. Authors in [61] present the architecture of a personalized TV system on the basis of TV-Anytime 2 metadata.…”

Section: Previous Research In Tv Program Recommendation Systemsmentioning

confidence: 99%

See 3 more Smart Citations

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

Martínez¹,

Costa-Montenegro²,

Burguillo³

et al. 2010

Information Sciences

319

118

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Section: Discussionmentioning

confidence: 99%

Section: Previous Research In Tv Program Recommendation Systemsmentioning

confidence: 98%

Section: Ratings Gatheringmentioning

confidence: 99%

“…More recent works in this area are [61,22,53,11]. Authors in [61] present the architecture of a personalized TV system on the basis of TV-Anytime 2 metadata.…”

Section: Previous Research In Tv Program Recommendation Systemsmentioning

confidence: 99%

See 2 more Smart Citations

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

Martínez¹,

Costa-Montenegro²,

Burguillo³

et al. 2010

Information Sciences

319

118

View full text Add to dashboard Cite

“…An example far from the TV domain is iCITY [14], a social recommender of events. Concerning the TV domain, we can report the work of [12], which provides TV recommendations using both content-based and social recommendations, the work of [34], which investigates the integration of a movie folksonomy with a semantic knowledge base about user-movie rentals, with the aim of using such information to define a better user profile, and the work of [8], a system which broadcasts user-generated videos in a mobile setting, merging personalization capabilities based on Semantic Web reasoning technologies and typical Web 2.0 features, such as sharing, annotating and rating items.…”

Section: Related Workmentioning

confidence: 99%

Supporting content discovery and organization in networks of contents and users

et al. 2010

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The design of approaches for supporting the user in the navigation of a variety of contents is an interesting area of research with many potential applications. In particular, interactive television (iTV) offers users the opportunity of accessing a huge amount of contents, ranging from general to specialized ones. As a consequence, the exploration of such contents must be supported in some way. In this paper, we present an innovative approach that integrates some recent methodologies and technologies developed in different areas of Artificial Intelligence and the Web: user-model-based adaptation, social networking, semantic annotation, and content sharing. We show how the integration of these technologies can provide interesting opportunities for a new approach to content navigation and discovery based on the possibility of exploring personalized networks of contents, users and concepts. Also, we focus on the specific goal of designing a platform for accessing iTV contents with the aim of providing the user with many alternative ways of exploring and discovering potentially interesting videos. After discussing the application, integrated in a project by Telecom Italia for a new paradigm of iTV, we preset the architecture we designed for integrating the methodologies discussed above.

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Viewer behaviors and practices in the (new) television environment

Abreu

Almeida

Teles

et al. 2013

Proceedings of the 11th European Conference on Interactive TV and Video

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This paper presents the main results of a study conducted in Portugal with the aim of identifying the current trends of the television ecosystem, with special relevance to understand the cognitive processes of viewers when selecting what to watch on TV. To this end, an online survey targeted mainly to young adults with mid to high academic degrees was designed. The survey gathered more than 600 responses, of which 550 were validated and taken into account in the data analysis. The results provide a detailed insight about the habits, dynamics and behaviors of spectators regarding the actual scenario of television. It also allowed the identification of the most relevant criteria considered by viewers when deciding what to watch on TV. The most important ones include the program genre followed by the state of mind (mood), the people who share the couch with and the time available to watch TV. The results also provide a structuring framework for the design of interactive television applications, targeted to this audience in the areas of personalization, recommendation and search and discovery of TV content.

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What Can I Watch on TV Tonight?

Cited by 3 publications

References 4 publications

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

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

Supporting content discovery and organization in networks of contents and users

Viewer behaviors and practices in the (new) television environment

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