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Revolutionizing Recommendations: The Power of Monolith’s Collisionless Embedding Tables
Manage episode 457078595 series 3351512
IVANCAST PODCAST에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 IVANCAST PODCAST 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
As part of the SHIFTERLABS Podcast project, where we turn scientific papers and articles into accessible, digestible audio content using Google Notebook LM, this episode demystifies cutting-edge AI innovations for a wider audience.
In this installment, we unpack Monolith, a groundbreaking real-time recommendation system developed by ByteDance. Explore how Monolith addresses the challenges of sparse and dynamic data, concept drift, and the need for scalable, real-time systems. Learn about its revolutionary collisionless embedding table, memory-efficient designs, and fault-tolerant online training mechanisms that ensure high-performance recommendations. Whether you’re an AI enthusiast or a curious learner, this episode sheds light on the future of personalized content delivery and AI-powered user experiences. Don’t miss this deep dive into the tech driving platforms like TikTok and beyond.
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