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
Autor Corporativo: Data Science Salon, publisher (publisher)
Otros Autores: Tran, Sylvia, on-screen presenter (onscreen presenter)
Formato: Vídeo online
Idioma:Inglés
Publicado: [Los Angeles, California] : Data Science Salon 2020.
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009822824506719
Descripción
Sumario:"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.
Notas:Title from resource description page (Safari, viewed November 4, 2020).
Descripción Física:1 online resource (1 streaming video file (19 min., 37 sec.)) : digital, sound, color