AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Industrial recommendation systems rely on multi-stage cascades whose retrieval, ranking, and serving components are difficult to replace jointly. We present GRP, a generative recommendation framework that combines retrieval, ranking, and reward modeling in a single encoder-decoder model, and evaluate a progressive path...
EGR is proposed, an Embedding-Native Generative Retrieval framework that uses a single shared LLM to learn item representations from item metadata and user representations from interaction histories in one embedding space, simplifying system design while improving retrieval quality and ad performance.
The design and launch of SnapLGR, an LLM-based generative retrieval system for short-video recommendation at Snapchat shows that successful production SnapLGR requires joint design across representation learning, vocabulary grounding, and efficient training and serving.
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