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Content similarity /

"Presented by Sylvia Tran, Data Scientist at Gracenote. User preferences and content similarity are both key to recommendation systems. While content similarity has been widely explored and utilized by many companies in the media & entertainment industries, it still remains relevant as the...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Formato: Electrónico Video
Idioma:Inglés
Publicado: [Los Angeles, California] : Data Science Salon, 2020.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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511 0 |a Presenter, Sylvia Tran. 
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520 |a "Presented by Sylvia Tran, Data Scientist at Gracenote. User preferences and content similarity are both key to recommendation systems. While content similarity has been widely explored and utilized by many companies in the media & entertainment industries, it still remains relevant as the amount of data and metadata available continues to grow and change. This talk discusses some of the challenges of content similarity and explores a few different attribute groups (aside from genre and cast) by which content similarity can be measured. Traditional attributes, like genre and cast alone, may not be as additive as they once were. More specifically, movies like Ted (starring Mark Wahlberg) and Shaun of the Dead do not neatly fit into a single genre. This talk also demonstrates how certain tried and true similarity metrics still yield meaningful and reasonably interpretable results for media & entertainment."--Resource description page 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Consumers' preferences. 
650 0 |a Decision making  |x Data processing. 
650 0 |a Natural language processing (Computer science) 
650 0 |a Digital media. 
650 6 |a Consommateurs  |x Préférences. 
650 6 |a Prise de décision  |x Informatique. 
650 6 |a Traitement automatique des langues naturelles. 
650 6 |a Médias numériques. 
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710 2 |a Data Science Salon,  |e publisher. 
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