Researchers have introduced a novel "Foundation-Expert paradigm" to more effectively deploy large foundation models across various recommender systems. This approach generates target-aware embeddings from a central foundation model, which are then utilized by lightweight, surface-specific expert models. This method significantly improves upon existing transfer learning techniques, achieving transfer ratios between 0.64 and 1.0 and outperforming knowledge distillation. The system has been successfully deployed at Meta, serving billions of daily requests and demonstrating measurable online metric improvements. AI
IMPACT This new paradigm could significantly improve the efficiency and effectiveness of deploying large foundation models in large-scale recommender systems.
RANK_REASON The cluster contains a research paper detailing a new paradigm for deploying foundation models in recommender systems, including deployment details at Meta. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Dai Li
- Foundation-Expert paradigm
- Gotit.pub
- Hugging Face
- Meta
- ScienceCast
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