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Xavi J

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liked a Space about 1 month ago
HuggingFaceTB/smol-training-playbook
upvoted a paper about 2 months ago
Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
reacted to singhsidhukuldeep's post with 🔥 about 1 year ago
Exciting breakthrough in large-scale recommendation systems! ByteDance researchers have developed a novel real-time indexing method called "Streaming Vector Quantization" (Streaming VQ) that revolutionizes how recommendations work at scale. >> Key Innovations Real-time Indexing: Unlike traditional methods that require periodic reconstruction of indexes, Streaming VQ attaches items to clusters in real time, enabling immediate capture of emerging trends and user interests. Superior Balance: The system achieves remarkable index balancing through innovative techniques like merge-sort modification and popularity-aware cluster assignment, ensuring all clusters participate effectively in recommendations. Implementation Efficiency: Built on VQ-VAE architecture, Streaming VQ features a lightweight and clear framework that makes it highly implementation-friendly for large-scale deployments. >> Technical Deep Dive The system operates in two key stages: - An indexing step using a two-tower architecture for real-time item-cluster assignment - A ranking step that employs sophisticated attention mechanisms and deep neural networks for precise recommendations. >> Real-world Impact Already deployed in Douyin and Douyin Lite, replacing all major retrievers and delivering significant user engagement improvements. The system handles a billion-scale corpus while maintaining exceptional performance and computational efficiency. This represents a significant leap forward in recommendation system architecture, especially for platforms dealing with dynamic, rapidly-evolving content. The ByteDance team's work demonstrates how rethinking fundamental indexing approaches can lead to substantial real-world improvements.
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