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How Spotify & Snap Use ML for Recommendation Systems

Posted Oct 21
# TransformX 2022
# Expert Panel
# AI in Retail & eCommerce
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SPEAKERS
Curtis Huang
Curtis Huang
Curtis Huang
Head of Content Understanding @ Snap

Curtis leads the content moderation and understanding group at Snap and has 17+ years of industry experience in building both consumer and enterprise products. Most recently before Snap, Curtis led the core ML and data foundation group at Facebook/Meta, responsible for content understanding, measurement capabilities, and ML platform. Highlights of his work included spearheading and launching multiple 0→1 initiatives, such as on-device AI, graph learning, and AI governance, and he was the co-organizer for the FB NLP Summit for several years. Previously, Curtis was a founding member of Gemini at Yahoo!, revamping the ML ranking and retrieval stack for mobile sponsored search. Prior to Yahoo!, Curtis led ML efforts in the innovation center at SAP Labs, focusing on natural language understanding, in-memory & distributed computing, and representation learning.

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Curtis leads the content moderation and understanding group at Snap and has 17+ years of industry experience in building both consumer and enterprise products. Most recently before Snap, Curtis led the core ML and data foundation group at Facebook/Meta, responsible for content understanding, measurement capabilities, and ML platform. Highlights of his work included spearheading and launching multiple 0→1 initiatives, such as on-device AI, graph learning, and AI governance, and he was the co-organizer for the FB NLP Summit for several years. Previously, Curtis was a founding member of Gemini at Yahoo!, revamping the ML ranking and retrieval stack for mobile sponsored search. Prior to Yahoo!, Curtis led ML efforts in the innovation center at SAP Labs, focusing on natural language understanding, in-memory & distributed computing, and representation learning.

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Tony Jebara
Tony Jebara
Tony Jebara
Head of Machine Learning and VP of Engineering at Spotify @ Spotify

Tony is the head of machine learning and vice president of engineering for personalization at Spotify. He leads a team of 100+ engineers and machine learners running Spotify's Home Page, Search Engine, Programming Platform, Voice Interface and User/Content Valuation. Tony also oversees the company-wide machine learning and AI strategy. Spotify’s ML strategy drives platform investments with new capabilities (across model training, serving, feature stores, logging and experimentation) as well as best practices in engineering and science. Previously, Tony was the director of machine learning at Netflix, where he launched improvements to many of its personalization algorithms. From 2001 to 2020, Tony was a tenured professor at Columbia University where he published hundreds of scientific articles with tens of thousands of citations. Tony holds a PhD from MIT.

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Tony is the head of machine learning and vice president of engineering for personalization at Spotify. He leads a team of 100+ engineers and machine learners running Spotify's Home Page, Search Engine, Programming Platform, Voice Interface and User/Content Valuation. Tony also oversees the company-wide machine learning and AI strategy. Spotify’s ML strategy drives platform investments with new capabilities (across model training, serving, feature stores, logging and experimentation) as well as best practices in engineering and science. Previously, Tony was the director of machine learning at Netflix, where he launched improvements to many of its personalization algorithms. From 2001 to 2020, Tony was a tenured professor at Columbia University where he published hundreds of scientific articles with tens of thousands of citations. Tony holds a PhD from MIT.

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SUMMARY

Content comes in many forms—from traditional media with professionally developed content to social media with user-generated content, and everything in between. Over the past decade, media platforms have evolved significantly to keep up with the pace of newly developing content and content trends. For example, social media now support multiple forms of content, including images and short-form video, and traditional media platforms have expanded to support in-house content and streamed content. This expert panel will explore the importance of recommendation systems, discuss the importance of the data that powers these systems in the ever-evolving media platform experience, and cover how and why recommendation systems are so critical for growing media-based businesses.

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Posted Jun 21 | Views 586
# Transform 2021