  Large-scale recommendation systems are characterized by their reliance on
high cardinality, heterogeneous features and the need to handle tens of
billions of user actions on a daily basis. Despite being trained on huge volume
of data with thousands of features, most Deep Learning Recommendation Models
(DLRMs) in industry fail to scale with compute.
  Inspired by success achieved by Transformers in language and vision domains,
we revisit fundamental design choices in recommendation systems. We reformulate
recommendation problems as sequential transduction tasks within a generative
modeling framework ("Generative Recommenders"), and propose a new architecture,
HSTU, designed for high cardinality, non-stationary streaming recommendation
data.
  HSTU outperforms baselines over synthetic and public datasets by up to 65.8%
in NDCG, and is 5.3x to 15.2x faster than FlashAttention2-based Transformers on
8192 length sequences. HSTU-based Generative Recommenders, with 1.5 trillion
parameters, improve metrics in online A/B tests by 12.4% and have been deployed
on multiple surfaces of a large internet platform with billions of users. More
importantly, the model quality of Generative Recommenders empirically scales as
a power-law of training compute across three orders of magnitude, up to
GPT-3/LLaMa-2 scale, which reduces carbon footprint needed for future model
developments, and further paves the way for the first foundational models in
recommendations.
