Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation

arXiv:2605.23191v1 Announce Type: new Abstract: Scaling recommendation models is a central challenge in recommender systems. Recently, RankMixer has emerged as an effective solution, operating on a unified token representation and alternating between token mixing and per-token feedforward networks (P-FFNs) to achieve scalable performance. However, RankMixer suffers from \textit{embedding collapse}, where learned representations have low effective rank, limiting expressivity and underutilizing the expanded representation space. Through empirical analysis and theoretical insights, we identify ri

Source: arXiv cs.LG — read the full report at the original publisher.

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