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		<title>FM-Intent: Predicting User Session Intent with Hierarchical Multi-Task Learning</title>
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		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Wed, 21 May 2025 16:28:07 +0000</pubDate>
				<category><![CDATA[AI]]></category>
		<category><![CDATA[deep learning]]></category>
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					<description><![CDATA[Authors: Sejoon Oh, Moumita Bhattacharya, Yesu Feng, Sudarshan Lamkhede, Ko-Jen Hsiao, and Justin BasilicoMotivationRecommender systems have become essential components of digital services across e-commerce, streaming media, and social networks [1, 2]....]]></description>
		
		
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		<title>Foundation Model for Personalized Recommendation</title>
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		<dc:creator><![CDATA[Netflix Technology Blog]]></dc:creator>
		<pubDate>Sat, 29 Mar 2025 00:51:25 +0000</pubDate>
				<category><![CDATA[AI]]></category>
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					<description><![CDATA[By Ko-Jen Hsiao, Yesu Feng and Sudarshan LamkhedeMotivationNetflix’s personalized recommender system is a complex system, boasting a variety of specialized machine learned models each catering to distinct needs including “Continue Watching” and “Today’...]]></description>
		
		
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		<title>New Research: Optimizing DAST Vulnerability Triage with Deep Learning</title>
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		<dc:creator><![CDATA[Tom Caiazza]]></dc:creator>
		<pubDate>Wed, 09 Nov 2022 16:00:00 +0000</pubDate>
				<category><![CDATA[Application Security]]></category>
		<category><![CDATA[artificial intelligence]]></category>
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					<description><![CDATA[In new paper, Rapid7 data scientists outline a novel deep learning model to automatically prioritize application security vulnerabilities and reduce false positive friction.]]></description>
		
		
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