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		<title>Causal Machine Learning for Creative Insights</title>
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		<pubDate>Sat, 25 Nov 2023 01:27:21 +0000</pubDate>
				<category><![CDATA[causal-inference]]></category>
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					<description><![CDATA[A framework to identify the causal impact of successful visual components.By Billur Engin, Yinghong Lan, Grace Tang, Cristina Segalin, Kelli Griggs, Vi IyengarIntroductionAt Netflix, we want our viewers to easily find TV shows and movies that resonate ...]]></description>
		
		
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		<title>AVA Discovery View: Surfacing Authentic Moments</title>
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		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 17 Aug 2023 22:07:14 +0000</pubDate>
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					<description><![CDATA[By: Hamid Shahid, Laura Johnson, Tiffany LowSynopsisAt Netflix, we have created millions of artwork to represent our titles. Each artwork tells a story about the title it represents. From our testing on promotional assets, we know which of these assets...]]></description>
		
		
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		<title>Detecting Scene Changes in Audiovisual Content</title>
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		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
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					<description><![CDATA[Avneesh Saluja, Andy Yao, Hossein TaghaviIntroductionWhen watching a movie or an episode of a TV show, we experience a cohesive narrative that unfolds before us, often without giving much thought to the underlying structure that makes it all possible. ...]]></description>
		
		
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		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Mon, 30 Jan 2023 16:16:03 +0000</pubDate>
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					<description><![CDATA[By Grace Tang, Aneesh Vartakavi, Julija Bagdonaite, Cristina Segalin, and Vi IyengarWhen members are shown a title on Netflix, the displayed artwork, trailers, and synopses are personalized. That means members see the assets that are most likely to hel...]]></description>
		
		
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		<title>Match Cutting at Netflix: Finding Cuts with Smooth Visual Transitions</title>
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		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Thu, 17 Nov 2022 16:37:01 +0000</pubDate>
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