  Recent advances in foundation models have led to a promising trend of
developing large recommendation models to leverage vast amounts of available
data. Still, mainstream models remain embarrassingly small in size and na\"ive
enlarging does not lead to sufficient performance gain, suggesting a deficiency
in the model scalability. In this paper, we identify the embedding collapse
phenomenon as the inhibition of scalability, wherein the embedding matrix tends
to occupy a low-dimensional subspace. Through empirical and theoretical
analysis, we demonstrate a \emph{two-sided effect} of feature interaction
specific to recommendation models. On the one hand, interacting with collapsed
embeddings restricts embedding learning and exacerbates the collapse issue. On
the other hand, interaction is crucial in mitigating the fitting of spurious
features as a scalability guarantee. Based on our analysis, we propose a simple
yet effective multi-embedding design incorporating embedding-set-specific
interaction modules to learn embedding sets with large diversity and thus
reduce collapse. Extensive experiments demonstrate that this proposed design
provides consistent scalability and effective collapse mitigation for various
recommendation models. Code is available at this repository:
https://github.com/thuml/Multi-Embedding.
