UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems
In recent years, the scaling laws of recommendation models have attracted increasing attention, which govern the relationship between performance and parameters/FLOPs of recommenders. Currently, there are three mainstream architectures for achieving scaling in recommendation models, namely attention-based, TokenMixer-based, and factorization-machine-based methods, which exhibit fundamental differences in both design philosophy and architectural structure. In this paper, we propose a unified scaling architecture for recommendation systems, namely \textbf{UniMixer}, to improve scaling efficiency and establish a unified theoretical framework that unifies the mainstream scaling blocks. By tra...
| Authors | Mingming Ha, Guanchen Wang, Linxun Chen et al. |
|---|---|
| Primary category | cs.IR |
| Published | 1 April 2026 |
| Last updated at source | 2 April 2026 |
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