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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Bibliographic Details
Corporate Author: Data Science Salon, publisher (publisher)
Other Authors: Tran, Sylvia, on-screen presenter (onscreen presenter)
Format: Online Video
Language:Inglés
Published: [Los Angeles, California] : Data Science Salon 2020.
Subjects:
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009822824506719
Description
Summary:"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.
Item Description:Title from resource description page (Safari, viewed November 4, 2020).
Physical Description:1 online resource (1 streaming video file (19 min., 37 sec.)) : digital, sound, color