SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation
Transformer architectures have achieved remarkable success across diverse domains; however, directly applying their standard self-attention mechanism to recommendation often yields suboptimal performance, sometimes even trailing behind well-designed simple recommendation models. In this paper, we reveal that this performance bottleneck stems from severe embedding and attention collapse unique to recommendation scenarios. The heterogeneity and long-tail nature of recommendation data lead to a severe spectral collapse dominated by a few principal singular values. We further theoretically demonstrate that this triggers a vicious cycle in recommendation model's forward and backward propagatio...
| Authors | Yu Cui, Yi Xu, Jiahao Wang et al. |
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| Primary category | cs.IR |
| Published | 27 July 2026 |
| Last updated at source | 3 August 2026 |
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