Recommendation Systems

Netflix's Big Bet: One model to rule recommendations: Yesu Feng, Netflix

Jul 16, 2025 · 22:28

Netflix staff research scientist Yesu Feng explains the company's bet on a single transformer-based foundation model for all recommendation use cases, replacing dozens of specialized models. He validates that scaling laws apply to recommendation systems, with the model growing from a few million profiles to 1 billion parameters, and that multi-token prediction (inspired by DeepSeek) notably improves metrics by forcing less myopic learning. The foundation model is consumed via three patterns: as a subgraph in downstream models, through precomputed embeddings (both member and content), and via fine-tuning or distillation. Feng reports that over the past 1.5 years, the model has been integrated into many applications, with A/B test wings showing high leverage and infrastructure consolidation. Future directions include universal representation for heterogeneous content types, generative retrieval for collection recommendation, and prompt tuning for faster adaptation.

Recsys Keynote: Improving Recommendation Systems & Search in the Age of LLMs - Eugene Yan, Amazon

Jul 16, 2025 · 20:54

Eugene Yan's keynote presents three innovations for recommendation systems: Semantic IDs, LLM-augmented data, and unified models. Kuaishou’s trainable multimodal Semantic IDs increased cold-start coverage by 3.6% and velocity by 3.5% by clustering content embeddings. Indeed used GPT-4 fine-tuning and distillation to filter bad job recommendations, reducing bad recs by 20% while boosting application rate 4% and cutting unsubscribes 5%. Spotify’s LLM-generated exploratory search queries drove a 9% increase in exploratory queries for new categories like podcasts. Netflix’s Unicorn unified ranker matched or exceeded specialized models across search and recommendations, while Etsy’s unified embeddings with a quality vector achieved a 2.6% sitewide conversion lift and 5% more search purchases.