meta-recsys/generative-recommenders

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1966 stars · 410 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Meta's research library implementing HSTU and M-FALCON from the ICML'24 paper 'Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations'. It reformulates classical deep learning recommendation models (DLRMs) as generative modeling and provides efficient training/inference for large-scale sequential recommenders.

Use cases

• train a generative sequential recommender model
• reproduce HSTU results on MovieLens and Amazon Reviews
• scale recommendation models to trillion parameters
• benchmark against SASRec, BERT4Rec, and GRU4Rec
• accelerate training and inference for large sequential recommenders
• study scaling laws for recommendation systems

When to choose

• you need state-of-the-art generative sequential recommendation models
• you want to reproduce or extend the HSTU/M-FALCON paper results
• you are researching scaling laws for billion-user recommender systems

When to avoid

• you need a production-ready plug-and-play recommender service
• you lack a GPU with 24GB+ memory
• you want a simple classical collaborative filtering solution

Facets

library · maturity active

machine-learning deep-learning llm-training benchmarking data-science machine-learning deep-learning data-science python recommender-systems recsys hstu generative-recommendations sequential-modeling transformers pytorch scaling-laws research-code icml-2024 algorithms linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="meta-recsys/generative-recommenders")