Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

arXiv:2402.17152v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.ir cs.lg daily data deep learning features generative high cardinality recommendation recommendations recommendation systems reliance scale speak systems type words