nUniMixer: Breaking the Barrier to a Unified and Scalable Backbone for Recommender Systems

In recent years, the scaling laws of recommendation models have attracted increasing attention. Three mainstream architectures have emerged for scaling ranking models: attention-based, TokenMixer-based, and factorization-machine-based architectures. These architectures differ fundamentally in both their design philosophies and structural characteristics. In this paper, we propose UniMixer, a unified theoretical framework for model scaling that establishes connections among these three mainstream blocks. By parameterizing the rule-based TokenMixer, we develop a Dual-Axis Heterogeneous Mixer (DAHM) that learns token-mixing patterns during training and achieves global element-wise feature mixing. To relax the constraints on the parameter matrices and further improve the return on investment (ROI) of model scaling, we further develop a normalized UniMixer block, termed nUniMixer. Extensive offline and online experiments across diverse tasks and application scenarios demonstrate the superior scalability of nUniMixer.