El nuevo negocio mediático liderado por Netflix: estudio del modelo y proyección en el mercado español

Castillo, Jéssica Izquierdo

doi:10.3145/epi.2015.nov.14

Cited by 38 publications

(22 citation statements)

References 4 publications

(4 reference statements)

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“…Netflix has opted to focus its business model on the demand for technical viewing quality, as well as on the mobile and accessibility capacity of content (Izquierdo-Castillo, 2015). This is why it evaluates the technological state of each playback (Govind, 2014):…”

Section: Netflix's Choice Of Data Sourcesmentioning

confidence: 99%

Data management in audiovisual business: Netflix as a case study

Fernández-Manzano¹,

Neira²,

Clares-Gavilán³

2016

EPI

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Section: Netflix's Choice Of Data Sourcesmentioning

confidence: 99%

Data management in audiovisual business: Netflix as a case study

Fernández-Manzano¹,

Neira²,

Clares-Gavilán³

2016

EPI

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“…Coprodução, Ficção, Netflix, Séries, Televisão. lo de negocio de la plataforma (Ojer & Capapé, 2012;Izquierdo-Castillo, 2015;Neira, 2015;Evens, 2014Páez, 2017Heredia-Ruiz, 2018) y en su vertiente publicitaria (Del Pino & Aguado, 2012;Pérez & Cornelio, 2018;Mayorga-Escalada, 2019), así como en la gestión de datos e información (Fernandez et al, 2016).…”

Section: Palavras-chaveunclassified

Netflix como productor audiovisual:

Hidalgo

2020

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Conocer los productos audiovisuales distribuidos por Netflix supone un escenario de estudio determinante, puesto que nos permite arrojar información sobre la estrategia aplicada para satisfacer el proceso de oferta-demanda entre el gigante audiovisual y sus públicos globales. El objetivo del presente artículo reside en ofrecer una radiografía sobre la producción propia de la plataforma de VOD Netflix, en concreto, desglosando y analizando formas y tendencias en una de las fórmulas de producción propia reconocida: la coproducción, con la finalidad de contribuir al desarrollo de un conocimiento específico sobre la cartera de productos propios de Netflix.

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“…Implicit feedback is reflected by records of purchases of goods and services. The use of such systems makes it easier for users to compare characteristics, as well as to choose a suitable item among many similar products [1]. Therefore, recommender systems are widely used as part of e-commerce systems, streaming services, when promoting online events, hotel reservations [2].…”

Section: Introductionmentioning

confidence: 99%

Detailing explanations in the recommender system based on matching temporal knowledge

Chalyi

Leshchynskyi

Leshchynska

2020

EEJET

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about users may be inaccurate or incomplete, which leads to distortion of recommendations. The received incorrect recommendations do not meet the interests of users, which reduces the credibility of the recommender system. Inaccuracies and distortions in user preference data result from shilling attacks [4]. Such attacks are used to change the sales of target items in the direction desired by the attacker. The essence of a shilling attack is to artificially change the ratings of the target items. As a result, a personal recommendation is formed with distortions and may not take into account the real preferences of users. The recommender system, using an incorrect personal list of goods and services, "forces" the user to choose those items that the attacker is interested in. In case of incomplete information about the user, recommendations are built for new or irregular users [5]. In such a cold start situation, there are no data obtained as a result of feedback from the user, since the latter has not yet made purchases or posted ratings. Therefore, at present, one of the trends in the development of recommender systems, which makes it possible to consider the incompleteness and inaccuracy of the initial data, is to

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El nuevo negocio mediático liderado por Netflix: estudio del modelo y proyección en el mercado español

Cited by 38 publications

References 4 publications

Data management in audiovisual business: Netflix as a case study

Data management in audiovisual business: Netflix as a case study

Netflix como productor audiovisual:

Detailing explanations in the recommender system based on matching temporal knowledge

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