From Sequences to Tables: Sequential Recommendation with Tabular Foundation Models

Sequential recommendation aims to predict users' next interactions from their historical behavior. Recent recommendation-specific prior-fitted networks (PFNs), pretrained on synthetic interaction sequences, have achieved strong empirical performance through in-context learning. Meanwhile, the PFN paradigm has also achieved strong results in tabular prediction, inspiring a range of tabular foundation models (TFMs). We investigate whether the general predictive capabilities of these TFMs can support sequential recommendation without recommendation-specific pretraining. To this end, we propose a reusable interface that reformulates next-item prediction as tabular candidate scoring. Our *content-aware feature construction* represents each history–candidate pair using item semantics and explicit short- and long-term relevance signals. These representations can be supplied to different TFM backbones, which condition on labeled historical examples to score and rank candidates while keeping their pretrained parameters fixed. Experiments on five benchmark datasets demonstrate strong recommendation performance, while ablation studies validate the effectiveness of our content-aware feature construction.