UniFormer introduces a unified model-centric scaling approach for recommender systems via feature-space and task-space modules, semantic tokenization, and multi-sequence attention, with reported gains in production A/B tests at Kuaishou.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
UniFormer: Efficient and Unified Model-Centric Scaling for Industrial Recommendation
UniFormer introduces a unified model-centric scaling approach for recommender systems via feature-space and task-space modules, semantic tokenization, and multi-sequence attention, with reported gains in production A/B tests at Kuaishou.